List of Publications of the Research Group of
Prof. Dr. Laurenz Wiskott

This list contains references to publications by Laurenz Wiskott and members of his group while they were working with him.

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Author	Year	Title	Reference	BibTeX type	Project
Aimone, J.B. & Wiskott, L.	2008	Computational modeling of Neurogenesis. [Abstract] [BibTeX] [URL]	Chapter 22 in Adult Neurogenesis , Cold Spring Harbor Monograph Series , 52, 463-481. Eds. Gage, F. H.; Kempermann, G. & Song, H. Publ. Cold Spring Harbor Laboratory Press, Woodbury, NY 11797, USA.	incollection	Adult neurogenesis: Function II (2005-2007)
Abstract: One of the most intriguing differences between ... [This book chapter has no abstract. Please folow the URL, select chapter 22, and read the introduction.]
BibTeX: @incollection{AimoneWiskott-2008, author = {James B. Aimone and Laurenz Wiskott}, title = {Computational modeling of Neurogenesis.}, booktitle = {Adult Neurogenesis}, publisher = {Cold Spring Harbor Laboratory Press}, year = {2008}, volume = {52}, pages = {463--481}, url = {http://books.google.com/books?id=5Kyahdob-NsC&printsec=frontcover&dq=Adult+neurogenesis&hl=en&src=bmrr&ei=M9n4Tej5B9DQsgbk58CKCQ&sa=X&oi=book_result&ct=result&resnum=1&ved=0CCkQ6AEwAA#v=onepage&q&f=false} }
Althoff, O.; Erdmann, A.; Wiskott, L. & Hertel, P.	1991	The Photorefractive Effect in LiNbO$_3$ at High Light Intensity. [Abstract] [BibTeX]	phys. stat. sol. (a) , 128, K41-K46.	article	Photorefractive effect in LiNbO3 (1989,1990)
Abstract: In lithium niobate waveguides and also in the bulk material, the refractive index change caused by a very high light intensity is much stronger than would be expected from measurements at low intensities. In this note we present a quantitative investigation of these phenomena and discuss some possible explanations.
BibTeX: @article{AlthoffErdmannEtAl-1991, author = {O. Althoff and A. Erdmann and L. Wiskott and P. Hertel}, title = {The Photorefractive Effect in LiNbO$_3$ at High Light Intensity.}, journal = {phys. stat. sol. (a)}, year = {1991}, volume = {128}, pages = {K41--K46} }
Appleby, P.A.; Kempermann, G. & Wiskott, L.	2011	The Role of Additive Neurogenesis and Synaptic Plasticity in a Hippocampal Memory Model with Grid-Cell Like Input. [Abstract] [BibTeX] [URL] [URL(2)]	PLoS Comput Biol , 7(1), e1001063. Publ. Public Library of Science.	article	Adult neurogenesis: Function III (2007-2009)
Abstract: Contrary to the long-standing belief that no new neurons are added to the adult brain, it is now known that new neurons are born in a number of different brain regions and animals. One such region is the hippocampus, an area that plays an important role in learning and memory. In this paper we explore the effect of adding new neurons in a computational model of rat hippocampal function. Our hypothesis is that adding new neurons helps in forming new memories without disrupting memories that have already been stored. We find that adding new units is indeed superior to either changing connectivity or allowing neuronal turnover (where old units die and are replaced). We then show that a more biologically plausible mechanism that combines all three of these processes produces the best performance. Our work provides a strong theoretical argument as to why new neurons are born in the adult hippocampus: the new units allow the network to adapt in a way that is not possible by rearranging existing connectivity using conventional plasticity or neuronal turnover.
BibTeX: @article{ApplebyKempermannEtAl-2011, author = {Appleby, Peter A. AND Kempermann, Gerd AND Wiskott, Laurenz}, title = {The Role of Additive Neurogenesis and Synaptic Plasticity in a Hippocampal Memory Model with Grid-Cell Like Input.}, journal = {PLoS Comput Biol}, publisher = {Public Library of Science}, year = {2011}, volume = {7}, number = {1}, pages = {e1001063}, url = {http://dx.doi.org/10.1371%2Fjournal.pcbi.1001063}, doi = {http://dx.doi.org/10.1371/journal.pcbi.1001063} }
Appleby, P.A.; Lezius, S.; Bandt, C.; Kempermann, G. & Wiskott, L.	2007	Neurogenesis avoids catastrophic interference in a sparsely coding dentate gyrus. [BibTeX]	Proc. 3rd Bernstein Symposium for Computational Neuroscience, Sep. 24-27, Göttingen, Germany , 41. Publ. Bernstein Center for Computational Neuroscience (BCCN) Göttingen.	inproceedings	Adult neurogenesis: Function II (2005-2007), Adult neurogenesis: Dynamics II (2006-now)
BibTeX: @inproceedings{ApplebyLeziusEtAl-2007a, author = {Peter A. Appleby and Susanne Lezius and Christoph Bandt and Gerd Kempermann and Laurenz Wiskott}, title = {Neurogenesis avoids catastrophic interference in a sparsely coding dentate gyrus.}, booktitle = {Proc. 3rd Bernstein Symposium for Computational Neuroscience, Sep. 24--27, Göttingen, Germany}, publisher = {Bernstein Center for Computational Neuroscience (BCCN) Göttingen}, year = {2007}, pages = {41} }
Appleby, P.A.; Lezius, S.; Kirste, I.; Bandt, C.; Kempermann, G. & Wiskott, L.	2007	Adult neurogenesis in the dentate gyrus: data analysis and modeling. [BibTeX]	Proc. Midterm Evaluation of the German National Network for Computational Neuroscience, Dec. 3-4, Berlin, Germany , 26.	inproceedings	Adult neurogenesis: Function II (2005-2007), Adult neurogenesis: Dynamics II (2006-now)
BibTeX: @inproceedings{ApplebyLeziusEtAl-2007b, author = {Peter A. Appleby and Susanne Lezius and Imke Kirste and Christoph Bandt and Gerd Kempermann and Laurenz Wiskott}, title = {Adult neurogenesis in the dentate gyrus: data analysis and modeling.}, booktitle = {Proc. Midterm Evaluation of the German National Network for Computational Neuroscience, Dec. 3--4, Berlin, Germany}, year = {2007}, pages = {26} }
Appleby, P.A. & Wiskott, L.	2009	Additive neurogenesis as a strategy for avoiding interference in a sparsely-coding dentate gyrus. [Abstract] [BibTeX] [URL] [URL(2)]	Network: Computation in Neural Systems , 20(3), 137-161.	article	Adult neurogenesis: Function II (2005-2007)
Abstract: Recently we presented a model of additive neurogenesis in a linear, feedforward neural network that performed an encoding-decoding memory task in a changing input environment. Growing the neural network over time allowed the network to adapt to changes in input statistics without disrupting retrieval properties, and we proposed that adult neurogenesis might fulfil a similar computational role in the dentate gyrus of the hippocampus. Here we explicitly evaluate this hypothesis by examining additive neurogenesis in a simplified hippocampal memory model. The model incorporates a divergence in unit number from the entorhinal cortex to the dentate gyrus and sparse coding in the dentate gyrus, both notable features of hippocampal processing. We evaluate two distinct adaptation strategies; neuronal turnover, where the network is of fixed size but units may be deleted and new ones added, and additive neurogenesis, where the network grows over time, and quantify the performance of the network across the full range of adaptation levels from zero in a fixed network to one in a fully adapting network. We find that additive neurogenesis is always superior to neuronal turnover as it permits the network to be responsive to changes in input statistics while at the same time preserving representations of earlier environments.
BibTeX: @article{ApplebyWiskott-2009, author = {Peter A. Appleby and Laurenz Wiskott}, title = {Additive neurogenesis as a strategy for avoiding interference in a sparsely-coding dentate gyrus.}, journal = {Network: Computation in Neural Systems}, year = {2009}, volume = {20}, number = {3}, pages = {137--161}, url = {http://informahealthcare.com/doi/abs/10.1080/09548980902993156} }
Appleby, P.; Kempermann, G. & Wiskott, L.	2010	The role of neurogenesis in the hippocampus. [BibTeX]	Adult Neurogenesis: Structure and Function, June, Frauenchiemsee, Germany .	inproceedings	Adult neurogenesis: Function III (2007-2010)
BibTeX: @inproceedings{ApplebyKempermannEtAl-2010, author = {Peter Appleby and Gerd Kempermann and Laurenz Wiskott}, title = {The role of neurogenesis in the hippocampus.}, booktitle = {Adult Neurogenesis: Structure and Function, June, Frauenchiemsee, Germany}, year = {2010} }
Appleby, P. & Wiskott, L.	2007	Additive neurogenesis as a strategy for avoiding catastrophic interference in a sparsely coding dentate gyrus. [BibTeX]	BCCN Symposium, March, Berlin, Germany . , Humboldt University Berlin	inproceedings	Adult neurogenesis: Function II (2005-2007)
BibTeX: @inproceedings{ApplebyWiskott-2007a, author = {Peter Appleby and Laurenz Wiskott}, title = {Additive neurogenesis as a strategy for avoiding catastrophic interference in a sparsely coding dentate gyrus.}, booktitle = {BCCN Symposium, March, Berlin, Germany}, year = {2007} }
Appleby, P. & Wiskott, L.	2007	The role of adult neurogenesis in the dentate gyrus. [BibTeX]	Perspectives in Computational Neuroscience Symposium, September, Göttingen, Germany . , MPI for Dynamics and Self-Organisation	inproceedings	Adult neurogenesis: Function II (2005-2007)
BibTeX: @inproceedings{ApplebyWiskott-2007b, author = {Peter Appleby and Laurenz Wiskott}, title = {The role of adult neurogenesis in the dentate gyrus.}, booktitle = {Perspectives in Computational Neuroscience Symposium, September, Göttingen, Germany}, year = {2007} }
Appleby, P. & Wiskott, L.	2006	Adult neurogenesis in the central nervous system. [BibTeX]	Berlin Neuroscience Forum 2006, August, Liebenwalde, Germany .	inproceedings	Adult neurogenesis: Function II (2005-2007)
BibTeX: @inproceedings{ApplebyWiskott-2006, author = {Peter Appleby and Laurenz Wiskott}, title = {Adult neurogenesis in the central nervous system.}, booktitle = {Berlin Neuroscience Forum 2006, August, Liebenwalde, Germany}, year = {2006} }
Bandt, C.; Beißwanger, E.; Wiskott, L. & Kempermann, G.	2005	A dynamical model for neural cell development. [BibTeX]	Proc. XXV Dynamics Days Europe 2005, July 25-28, Berlin, Germany , Europhysics Conference Series , 29 E, 233. Eds. Schöll, E. & Lüdge, K.	inproceedings	Adult Neurogenesis: Dynamics I (2004,2005)
BibTeX: @inproceedings{BandtBeisswangerEtAl-2005, author = {Christoph Bandt and Elena Beißwanger and Laurenz Wiskott and Gerd Kempermann}, title = {A dynamical model for neural cell development.}, booktitle = {Proc. XXV Dynamics Days Europe 2005, July 25--28, Berlin, Germany}, year = {2005}, volume = {29 E}, pages = {233} }
Beißwanger, E.	2005	Modeling adult neurogenesis in the hippocampus. [Abstract] [BibTeX]	Diploma thesis, Department of Mathematics and Computer Science, Ernst-Moritz-Arndt-University Greifswald, Germany .	mastersthesis	Adult Neurogenesis: Dynamics I (2004,2005)
Abstract: It is a distinctive feature of the hippocampus of the mammalian brain to generate new neurons throughout life. Neuronal progenitor cells pass through several steps of maturation until they reach adulthood and full functionality. In a mouse model six developmental stages have been defined which represent consecutive phases of adult neurogenesis. The early stages are highly proliferative, then the cells become postmitotic. Using the thymidine substitute BrdU as a marker for proliferating cells permits to detect the number of cells in every stage at consecutive times. The resulting test series indicate the dynamics of neuronal development. In the study we presented here we examine the reliability of the observed cell counts, discussing the experimental design, which has been used and especially attending the problems of the BrdU labeling method. Subsequently we establish a simple model for adult neurogenesis based on a system of linear ordinary differential equations. In a second approach we apply a discrete model, based on Leslie matrices. We analyze different scenarios of neuronal development to find out which one fits the data best and consequently might describe the real situation.
BibTeX: @mastersthesis{Beisswanger-2005, author = {Elena Beißwanger}, title = {Modeling adult neurogenesis in the hippocampus.}, school = {Department of Mathematics and Computer Science}, year = {2005} }
Berkes, P.	2005	Pattern recognition with slow feature analysis. [BibTeX] [URL]	Cognitive Sciences EPrint Archive (CogPrints) , 4104.	misc	Handwritten digit recognition (2005)
BibTeX: @misc{Berkes-2005a, author = {Pietro Berkes}, title = {Pattern recognition with slow feature analysis.}, year = {2005}, volume = {4104}, howpublished = {Cognitive Sciences EPrint Archive (CogPrints)}, url = {http://cogprints.org/4104/} }
Berkes, P.	2005	Handwritten digit recognition with Nonlinear Fisher Discriminant Analysis. [BibTeX]	Proc. Intl. Conf. on Artificial Neur. Netw. (ICANN'05) , Lecture Notes on Computer Science , 3696(2), 285-287. Publ. Springer.	inproceedings
BibTeX: @inproceedings{Berkes-2005b, author = {Berkes, Pietro}, title = {Handwritten digit recognition with Nonlinear Fisher Discriminant Analysis.}, booktitle = {Proc. Intl. Conf. on Artificial Neur. Netw. (ICANN'05)}, publisher = {Springer}, year = {2005}, volume = {3696}, number = {2}, pages = {285--287} }
Berkes, P.	2005	Temporal slowness as an unsupervised learning principle: self-organization of complex-cell receptive fields and application to pattern recognition. [BibTeX]	PhD thesis, Institute for Biology, Humboldt University Berlin, D-10099 Berlin, Germany .	phdthesis	Handwritten digit recognition (2005), Analysis of quadratic forms (2002-2004)
BibTeX: @phdthesis{Berkes-2005c, author = {Pietro Berkes}, title = {Temporal slowness as an unsupervised learning principle: self-organization of complex-cell receptive fields and application to pattern recognition.}, school = {Institute for Biology}, year = {2005} }
Berkes, P. & Wiskott, L.	2002	Applying Slow Feature Analysis to Image Sequences Yields a Rich Repertoire of Complex Cell Properties. [BibTeX] [URL(2)]	Proc. Intl. Conf. on Artificial Neural Networks (ICANN'02) , Lecture Notes in Computer Science , 81-86. Ed. Dorronsoro, J. R. Publ. Springer.	inproceedings	SFA: Complex cells (2001-2003)
BibTeX: @inproceedings{BerkesWiskott-2002, author = {Pietro Berkes and Laurenz Wiskott}, title = {Applying Slow Feature Analysis to Image Sequences Yields a Rich Repertoire of Complex Cell Properties.}, booktitle = {Proc. Intl. Conf. on Artificial Neural Networks (ICANN'02)}, publisher = {Springer}, year = {2002}, pages = {81--86} }
Berkes, P. & Wiskott, L.	2003	Slow feature analysis yields a rich repertoire of complex-cells properties. [BibTeX]	Proc. 29th Göttingen Neurobiology Conference, Göttingen, Germany , 602-603. Eds. Elsner, N. & Zimmermann, H. Publ. Georg Thieme Verlag, Stuttgart.	inproceedings	SFA: Complex cells (2001-2003)
BibTeX: @inproceedings{BerkesWiskott-2003b, author = {Pietro Berkes and Laurenz Wiskott}, title = {Slow feature analysis yields a rich repertoire of complex-cells properties.}, booktitle = {Proc. 29th Göttingen Neurobiology Conference, Göttingen, Germany}, publisher = {Georg Thieme Verlag}, year = {2003}, pages = {602--603} }
Berkes, P. & Wiskott, L.	2007	Analysis and interpretation of quadratic models of receptive fields. [Abstract] [BibTeX] [URL]	Nature Protocols , 2(2), 400-407.	article	Analysis of quadratic forms (2002-2004)
Abstract: In this protocol, we present a procedure to analyze and visualize models of neuronal input-output functions that have a quadratic, a linear and a constant term, to determine their overall behavior. The suggested interpretations are close to those given by physiological studies of neurons, making the proposed methods particularly suitable for the analysis of receptive fields resulting from physiological measurements or model simulations.
BibTeX: @article{BerkesWiskott-2007, author = {Berkes, P. and Wiskott, L.}, title = {Analysis and interpretation of quadratic models of receptive fields.}, journal = {Nature Protocols}, year = {2007}, volume = {2}, number = {2}, pages = {400--407}, url = {http://dx.doi.org/10.1038/nprot.2007.27}, doi = {http://dx.doi.org/10.1038/nprot.2007.27} }
Berkes, P. & Wiskott, L.	2006	On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields. [Abstract] [BibTeX] [URL] [URL(2)]	Neural Computation , 18(8), 1868-1895.	article	Analysis of quadratic forms (2002-2004)
Abstract: In this letter, we introduce some mathematical and numerical tools to analyze and interpret inhomogeneous quadratic forms. The resulting characterization is in some aspects similar to that given by experimental studies of cortical cells, making it particularly suitable for application to second-order approximations and theoretical models of physiological receptive fields. We first discuss two ways of analyzing a quadratic form by visualizing the coefficients of its quadratic and linear term directly and by considering the eigenvectors of its quadratic term. We then present an algorithm to compute the optimal excitatory and inhibitory stimuli?those that maximize and minimize the considered quadratic form, respectively, given a fixed energy constraint. The analysis of the optimal stimuli is completed by considering their invariances, which are the transformations to which the quadratic form is most insensitive, and by introducing a test to determine which of these are statistically significant. Next we propose a way to measure the relative contribution of the quadratic and linear term to the total output of the quadratic form. Furthermore, we derive simpler versions of the above techniques in the special case of a quadratic form without linear term. In the final part of the letter, we show that for each quadratic form, it is possible to build an equivalent two-layer neural network, which is compatible with (but more general than) related networks used in some recent articles and with the energy model of complex cells. We show that the neural network is unique only up to an arbitrary orthogonal transformation of the excitatory and inhibitory subunits in the first layer.
BibTeX: @article{BerkesWiskott-2006, author = {Pietro Berkes and Laurenz Wiskott}, title = {On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields.}, journal = {Neural Computation}, year = {2006}, volume = {18}, number = {8}, pages = {1868--1895}, url = {http://dx.doi.org/10.1162/neco.2006.18.8.1868}, doi = {http://dx.doi.org/10.1162/neco.2006.18.8.1868} }
Berkes, P. & Wiskott, L.	2005	On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields. [BibTeX] [URL]	Cognitive Sciences EPrint Archive (CogPrints) , 4081.	misc	Analysis of quadratic forms (2002-2004)
BibTeX: @misc{BerkesWiskott-2005a, author = {Pietro Berkes and Laurenz Wiskott}, title = {On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields.}, year = {2005}, volume = {4081}, howpublished = {Cognitive Sciences EPrint Archive (CogPrints)}, url = {http://cogprints.org/4081/} }
Berkes, P. & Wiskott, L.	2005	Analysis of inhomogeneous quadratic forms for physiological and theoretical studies. [BibTeX]	Proc. Computational and Systems Neuroscience (COSYNE'05), Salk Lake City, USA .	inproceedings	Analysis of quadratic forms (2002-2004)
BibTeX: @inproceedings{BerkesWiskott-2005b, author = {Pietro Berkes and Laurenz Wiskott}, title = {Analysis of inhomogeneous quadratic forms for physiological and theoretical studies.}, booktitle = {Proc. Computational and Systems Neuroscience (COSYNE'05), Salk Lake City, USA}, year = {2005} }
Berkes, P. & Wiskott, L.	2005	Slow feature analysis yields a rich repertoire of complex cell properties. [Abstract] [BibTeX] [URL] [URL(2)]	Journal of Vision , 5(6), 579-602.	article	SFA: Complex cells (2001-2003)
Abstract: In this study, we investigate temporal slowness as a learning principle for receptive fields using slow feature analysis, a new algorithm to determine functions that extract slowly varying signals from the input data. We find a good qualitative and quantitative match between the set of learned functions trained on image sequences and the population of complex cells in the primary visual cortex (V1). The functions show many properties found also experimentally in complex cells, such as direction selectivity, non-orthogonal inhibition, end-inhibition, and side-inhibition. Our results demonstrate that a single unsupervised learning principle can account for such a rich repertoire of receptive field properties.
BibTeX: @article{BerkesWiskott-2005c, author = {Pietro Berkes and Laurenz Wiskott}, title = {Slow feature analysis yields a rich repertoire of complex cell properties.}, journal = {Journal of Vision}, year = {2005}, volume = {5}, number = {6}, pages = {579--602}, url = {http://journalofvision.org/5/6/9/}, doi = {http://dx.doi.org/10.1167/5.6.9} }
Berkes, P. & Wiskott, L.	2004	Slow feature analysis yields a rich repertoire of complex-cells properties. [BibTeX]	Proc. Early Cognitive Vision Workshop, May 28-June 1, Isle Of Skye, Scotland .	inproceedings	SFA: Complex cells (2001-2003)
BibTeX: @inproceedings{BerkesWiskott-2004, author = {Pietro Berkes and Laurenz Wiskott}, title = {Slow feature analysis yields a rich repertoire of complex-cells properties.}, booktitle = {Proc. Early Cognitive Vision Workshop, May 28--June 1, Isle Of Skye, Scotland}, year = {2004} }
Berkes, P. & Wiskott, L.	2003	Slow feature analysis yields a rich repertoire of complex-cell properties. [BibTeX] [URL]	Cognitive Sciences EPrint Archive (CogPrints) , 2804.	misc	SFA: Complex cells (2001-2003)
BibTeX: @misc{BerkesWiskott-2003a, author = {Pietro Berkes and Laurenz Wiskott}, title = {Slow feature analysis yields a rich repertoire of complex-cell properties.}, year = {2003}, volume = {2804}, howpublished = {Cognitive Sciences EPrint Archive (CogPrints)}, url = {http://cogprints.org/2804/} }
Berkes, P. & Zito, T.	2007	Modular toolkit for Data Processing (MDP version 2.1). [BibTeX]	http://mdp-toolkit.sourceforge.net/ .	misc	MDP: Modular toolkit for data processing (2003-now)
BibTeX: @misc{BerkesZito-2007, author = {Pietro Berkes and Tiziano Zito}, title = {Modular toolkit for Data Processing (MDP version 2.1).}, year = {2007}, howpublished = {http://mdp-toolkit.sourceforge.net/} }
Berkes, P. & Zito, T.	2005	Modular toolkit for Data Processing (MDP). [BibTeX]	Proc. Europython 2005, June 27-29, Gothenburg .	inproceedings	MDP: Modular toolkit for data processing (2003-now)
BibTeX: @inproceedings{BerkesZito-2005, author = {P. Berkes and T. Zito}, title = {Modular toolkit for Data Processing (MDP).}, booktitle = {Proc. Europython 2005, June 27--29, Gothenburg}, year = {2005} }
Blaschke, T.	2005	Independent component analysis and slow feature analysis: relations and combination. [BibTeX] [URL]	PhD thesis, Institute for Physics, Humboldt University Berlin, D-10099 Berlin, Germany .	phdthesis	Improved cumulant based ICA (2001,2002), SFA versus ICA (2002-2004), Independent slow feature analysis (ISFA) (2003-2005)
BibTeX: @phdthesis{Blaschke-2005, author = {Tobias Blaschke}, title = {Independent component analysis and slow feature analysis: relations and combination.}, school = {Institute for Physics}, year = {2005}, url = {http://edoc.hu-berlin.de/docviews/abstract.php?lang=ger&id=25458} }
Blaschke, T.; Berkes, P. & Wiskott, L.	2006	What is the relationship between slow feature analysis and independent component analysis? [Abstract] [BibTeX] [URL] [URL(2)]	Neural Computation , 18(10), 2495-2508.	article	SFA versus ICA (2002-2004)
Abstract: We present an analytical comparison between linear slow feature analysis and second-order independent component analysis, and show that in the case of one time delay the two approaches are equivalent. We also consider the case of several time delays and discuss two possible extensions of slow feature analysis.
BibTeX: @article{BlaschkeBerkesEtAl-2006, author = {T. Blaschke and P. Berkes and L. Wiskott}, title = {What is the relationship between slow feature analysis and independent component analysis?}, journal = {Neural Computation}, year = {2006}, volume = {18}, number = {10}, pages = {2495--2508}, url = {http://www.mitpressjournals.org/doi/abs/10.1162/neco.2006.18.10.2495} }
Blaschke, T. & Wiskott, L.	2002	An Improved Cumulant Based Method for Independent Component Analysis. [BibTeX]	Proc. Intl. Conf. on Artificial Neural Networks (ICANN'02) , Lecture Notes in Computer Science , 1087-1093. Ed. Dorronsoro, J. R. Publ. Springer.	inproceedings	Improved cumulant based ICA (2001,2002)
BibTeX: @inproceedings{BlaschkeWiskott-2002, author = {Tobias Blaschke and Laurenz Wiskott}, title = {An Improved Cumulant Based Method for Independent Component Analysis.}, booktitle = {Proc. Intl. Conf. on Artificial Neural Networks (ICANN'02)}, publisher = {Springer}, year = {2002}, pages = {1087--1093} }
Blaschke, T. & Wiskott, L.	2005	Nonlinear Blind Source Separation by Integrating Independent Component Analysis and Slow Feature Analysis. [BibTeX]	Proc. Advances in Neural Information Processing Systems 17 (NIPS'04) , 177-184. Eds. Saul, L. K.; Weiss, Y. & Bottou, L. Publ. The MIT Press.	inproceedings	Independent slow feature analysis (ISFA) (2003-2005)
BibTeX: @inproceedings{BlaschkeWiskott-2005, author = {T. Blaschke and L. Wiskott}, title = {Nonlinear Blind Source Separation by Integrating Independent Component Analysis and Slow Feature Analysis.}, booktitle = {Proc. Advances in Neural Information Processing Systems 17 (NIPS'04)}, publisher = {The MIT Press}, year = {2005}, pages = {177--184} }
Blaschke, T. & Wiskott, L.	2004	CuBICA: Independent Component Analysis by Simultaneous Third- and Fourth-Order Cumulant Diagonalization. [Abstract] [BibTeX] [URL] [URL(2)]	IEEE Transactions on Signal Processing , 52(5), 1250-1256.	article	Improved cumulant based ICA (2001,2002)
Abstract: CuBICA, an improved method for independent component analysis (ICA) based on the diagonalization of cumulant tensors is proposed. It is based on Comon's algorithm [Comon, 1994] but it takes third- and fourth-order cumulant tensors into account simultaneously. The underlying contrast function is also mathematically much simpler and has a more intuitive interpretation. It is therefore easier to optimize and approximate. A comparison with Comon's and three other ICA-algorithms on different data sets demonstrates its performance.
BibTeX: @article{BlaschkeWiskott-2004a, author = {T. Blaschke and L. Wiskott}, title = {CuBICA: Independent Component Analysis by Simultaneous Third- and Fourth-Order Cumulant Diagonalization.}, journal = {IEEE Transactions on Signal Processing}, year = {2004}, volume = {52}, number = {5}, pages = {1250--1256}, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1284823} }
Blaschke, T. & Wiskott, L.	2004	Independent Slow Feature Analysis and Nonlinear Blind Source Separation. [BibTeX] [URL(2)]	Proc. of the 5th Int. Conf. on Independent Component Analysis and Blind Signal Separation (ICA'04), Granada, Spain , Lecture Notes in Computer Science . Publ. Springer.	inproceedings	Independent slow feature analysis (ISFA) (2003-2005)
BibTeX: @inproceedings{BlaschkeWiskott-2004b, author = {T. Blaschke and L. Wiskott}, title = {Independent Slow Feature Analysis and Nonlinear Blind Source Separation.}, booktitle = {Proc. of the 5th Int. Conf. on Independent Component Analysis and Blind Signal Separation (ICA'04), Granada, Spain}, publisher = {Springer}, year = {2004} }
Blaschke, T. & Wiskott, L.	2003	CuBICA: Independent Component Analysis by Simultaneous Third- and Fourth-Order Cumulant Diagonalization. [BibTeX] [URL]	Computer Science Preprint Server (CSPS): Computational Intelligence/0304002 .	misc	Improved cumulant based ICA (2001,2002)
BibTeX: @misc{BlaschkeWiskott-2003, author = {Tobias Blaschke and Laurenz Wiskott}, title = {CuBICA: Independent Component Analysis by Simultaneous Third- and Fourth-Order Cumulant Diagonalization.}, year = {2003}, howpublished = {Computer Science Preprint Server (CSPS): Computational Intelligence/0304002}, url = {http://www.compscipreprints.com/comp/Preprint/blaschke/20030409/1/} }
Blaschke, T.; Zito, T. & Wiskott, L.	2007	Independent Slow Feature Analysis and Nonlinear Blind Source Separation. [Abstract] [BibTeX] [URL] [URL(2)]	Neural Computation , 19(4), 994-1021.	article	Independent slow feature analysis (ISFA) (2003-2005)
Abstract: In the linear case statistical independence is a sufficient criterion for performing blind source separation. In the nonlinear case, however, it leaves an ambiguity in the solutions that has to be resolved by additional criteria. Here we argue that temporal slowness complements statistical independence well and that a combination of the two leads to unique solutions of the nonlinear blind source separation problem. The algorithm we present is a combination of second-order Independent Component Analysis and Slow Feature Analysis and is referred to as Independent Slow Feature Analysis. Its performance is demonstrated on nonlinearly mixed music data. We conclude that slowness is indeed a useful complement to statistical independence but that time-delayed second-order moments are only a weak measure of statistical independence.
BibTeX: @article{BlaschkeZitoEtAl-2007, author = {Tobias Blaschke and Tiziano Zito and Laurenz Wiskott}, title = {Independent Slow Feature Analysis and Nonlinear Blind Source Separation.}, journal = {Neural Computation}, year = {2007}, volume = {19}, number = {4}, pages = {994--1021}, url = {http://neco.mitpress.org/cgi/content/abstract/19/4/994} }
Creutzig, F. & Sprekeler, H.	2008	Predictive Coding and the Slowness Principle: An Information-Theoretic Approach. [Abstract] [BibTeX] [URL]	Neural Computation , 20(4), 1026-1041.	article	N.N.
Abstract: Understanding the guiding principles of sensory coding strategies is a main goal in computational neuroscience. Among others, the principles of predictive coding and slowness appear to capture aspects of sensory processing. Predictive coding postulates that sensory systems are adapted to the structure of their input signals such that information about future inputs is encoded. Slow feature analysis (SFA) is a method for extracting slowly varying components from quickly varying input signals, thereby learning temporally invariant features. Here, we use the information bottleneck method to state an information-theoretic objective function for temporally local predictive coding. We then show that the linear case of SFA can be interpreted as a variant of predictive coding that maximizes the mutual information between the current output of the system and the input signal in the next time step. This demonstrates that the slowness principle and predictive coding are intimately related.
BibTeX: @article{CreutzigSprekeler-2008, author = {Creutzig, Felix and Sprekeler, Henning}, title = {Predictive Coding and the Slowness Principle: An Information-Theoretic Approach.}, journal = {Neural Computation}, year = {2008}, volume = {20}, number = {4}, pages = {1026--1041}, url = {http://dx.doi.org/10.1162/neco.2008.01-07-455}, doi = {http://dx.doi.org/10.1162/neco.2008.01-07-455} }
Dähne, S.	2010	Self-organization Of V1 Complex-Cells Based On Slow Feature Analysis And Retinal Waves. [Abstract] [BibTeX] [URL(2)]	Bernstein Center for Computational Neuroscience, Berlin Institute of Technology .	mastersthesis	SFA: Prenatal complex cells (2008-2010)
Abstract: The developing visual system of many mammalian species is partially structured and organized even before the onset of vision. Spontaneous neural activity, which spreads in waves across the retina, has been suggested to play a major role in these prenatal struc- turing processes. Recently, it has been shown that when employing an efficient coding strategy, such as sparse coding, these retinal activity patterns lead to basis functions that resemble optimal stimuli of simple cells in primary visual cortex (V1). Here I present the results of applying a coding strategy that optimizes for temporal slowness, namely Slow Feature Analysis (SFA), to a biologically plausible model of retinal waves. Previously, SFA has been successfully applied in modeling parts of the visual system, most notably in reproducing a rich set of complex-cell features by training SFA with natural image sequences. In this work, I was able to obtain units that share a number of properties with cortical complex-cells by training with simulated retinal waves. The results support the idea that retinal waves share relevant temporal and spatial properties with natural visual input. Hence, retinal waves seem suitable training stimuli to learn invariances and thereby shape the developing early visual system so that it is best prepared for coding input from the natural world.
BibTeX: @mastersthesis{Dahne-2010, author = {Dähne, Sven}, title = {Self-organization Of V1 Complex-Cells Based On Slow Feature Analysis And Retinal Waves.}, school = {Bernstein Center for Computational Neuroscience, Berlin Institute of Technology}, year = {2010} }
Doursat, R.; Konen, W.; Lades, M.; von der Malsburg, C.; Vorbrüggen, J.; Wiskott, L. & Würtz, R.P.	1993	Neural Mechanisms of Elastic Pattern Matching. [BibTeX]	Technical report , IR-INI 93-01. Publ. Institut für Neuroinformatik, Ruhr University Bochum, D-44780 Bochum, Germany.	techreport	Scene analysis (1992)
BibTeX: @techreport{DoursatKonenEtAl-1993, author = {René Doursat and Wolfgang Konen and Martin Lades and Christoph von der Malsburg and Jan Vorbrüggen and Laurenz Wiskott and Rolf P. Würtz}, title = {Neural Mechanisms of Elastic Pattern Matching.}, publisher = {Institut für Neuroinformatik}, year = {1993}, volume = {IR-INI 93-01}, howpublished = {Technical report} }
Dähne, S.; Wilbert, N. & Wiskott, L.	2010	Learning Complex Cell Units From Simulated Prenatal Retinal Waves Using Slow Feature Analysis. [BibTeX]	Interdisciplinary College 2010 . Eds. Porzel, R.; Sebanz, N. & Spitzer, M.	conference	SFA: Prenatal complex cells (2008-2010)
BibTeX: @conference{DahneWilbertEtAl-2010b, author = {Dähne, Sven and Wilbert, Niko and Wiskott, Laurenz}, title = {Learning Complex Cell Units From Simulated Prenatal Retinal Waves Using Slow Feature Analysis.}, booktitle = {Interdisciplinary College 2010}, year = {2010} }
Dähne, S.; Wilbert, N. & Wiskott, L.	2010	Self-organization of V1 Complex Cells Based On Slow Feature Analysis And Retinal Waves. [BibTeX]	Proc. Bernstein Conference on Computational Neuroscience, Sep 27-Oct 1, Berlin, Germany .	inproceedings	SFA: Prenatal complex cells (2008-2010)
BibTeX: @inproceedings{DahneWilbertEtAl-2010a, author = {S. Dähne and N. Wilbert and L. Wiskott}, title = {Self-organization of V1 Complex Cells Based On Slow Feature Analysis And Retinal Waves.}, booktitle = {Proc. Bernstein Conference on Computational Neuroscience, Sep 27--Oct 1, Berlin, Germany}, year = {2010} }
Dähne, S.; Wilbert, N. & Wiskott, L.	2009	Learning complex cell units from simulated prenatal retinal waves with slow feature analysis. [Abstract] [BibTeX] [URL] [URL(3)]	Proc. 18th Annual Computational Neuroscience Meeting (CNS'09), July 18-23, Berlin, Germany .	inproceedings	SFA: Prenatal complex cells (2008-2010)
Abstract: Many properties of the developing visual system are structured and organized before the onset of vision. Spontaneous neural activity, which spreads in waves across the retina, has been suggested to play a major role in these prenatal structuring processes [1]. Recently, it has been shown that when employing an efficient coding strategy, such as sparse coding, these retinal activity patterns lead to basis functions that resemble optimal stimuli of simple cells in V1 [2]. Here we present the results of applying a coding strategy that optimizes for temporal slowness, namely Slow Feature Analysis (SFA) [3], to a biologically plausible model of retinal waves [4] (see figure 1). We also tested other wave-like inputs (sinusoidal waves, moving Gauss blobs) that allow for an analytical understanding of the results. Previously, SFA has been successfully applied in modeling parts of the visual system, most notably in reproducing a rich set of complex cell features by training SFA with natural image sequences [5]. In this work, we were able to obtain complex-cell like receptive fields in all input conditions, as displayed in figure 2. [Figure] Figure 1. Retinal wave training sequence. Snapshots of an image sequence that was generated by the retinal wave model described in [1] and used as input to SFA. A white square in the top left corner of the first image indicates the receptive field size. [Figure] Figure 2. A sample of optimal stimuli of quadratic functions found by SFA, after training with different inputs. Training sequences derived from natural images and pink noise images result in optimal stimuli (A and B, respectively) that exhibit complex cell properties as expected (compare [2]). Training with discretized moving Gaussian blobs and the retinal wave model results in optimal stimuli (C and D, respectively) that are similar to those in (A) and (B). All units show phase invariance similar to complex cells. Our results support the idea that retinal waves share relevant temporal and spatial properties with natural images. Hence, retinal waves seem suitable training stimuli to learn invariances and thereby shape the developing early visual system so that it is best prepared for coding input from the natural world. References 1. Wong ROL: Retinal waves and visual system development. Annu. Rev. Neurosci 1999, 22:28-47. 2. Albert MV, Schnabel A, Field DJ: Innate visual learning through spontaneous activity patterns. PLoS Comput Biol 2008., 4. 3. Wiskott L, Sejnowski TJ: Slow feature analysis: unsupervised learning of invariances. Neural Computation 2002, 14:715-770. 4. Godfrey KB, Swindale NV: Retinal wave behavior through activity-dependent refractory periods. PLoS Comput Biol 2007, 3:2408-2420. 5. Berkes P, Wiskott L: Slow feature analysis yields a rich repertoire of complex cell properties. J. Vision 2005, 5:579-602.
BibTeX: @inproceedings{DahneWilbertEtAl-2009a, author = {Sven Dähne and Niko Wilbert and Laurenz Wiskott}, title = {Learning complex cell units from simulated prenatal retinal waves with slow feature analysis.}, booktitle = {Proc. 18th Annual Computational Neuroscience Meeting (CNS'09), July 18--23, Berlin, Germany}, year = {2009}, url = {http://www.biomedcentral.com/1471-2202/10/S1/P129}, doi = {http://dx.doi.org/10.1186/1471-2202-10-S1-P129} }
Dähne, S.; Wilbert, N. & Wiskott, L.	2009	Learning complex cell units from simulated prenatal retinal waves with slow feature analysis. [BibTeX]	Proc. 6'th International PhD Symposium Berlin Brain Days, Dec. 9-11, Berlin, Germany .	inproceedings	SFA: Prenatal complex cells (2008-2010)
BibTeX: @inproceedings{DahneWilbertEtAl-2009b, author = {Sven Dähne and Niko Wilbert and Laurenz Wiskott}, title = {Learning complex cell units from simulated prenatal retinal waves with slow feature analysis.}, booktitle = {Proc. 6'th International PhD Symposium Berlin Brain Days, Dec. 9--11, Berlin, Germany}, year = {2009} }
Escalante, A. & Wiskott, L.	2010	Gender and Age Estimation from Synthetic Face Images with Hierarchical Slow Feature Analysis. [Abstract] [BibTeX] [URL] [URL(2)]	International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'10), June 28-July 2, Dortmund . Eds. Hüllermeier, E. & Kruse, R.	inproceedings	Face processing with SFA (2009-now)
Abstract: Our ability to recognize the gender and estimate the age of people around us is crucial for our social development and interactions. In this paper, we investigate how to use Slow Feature Analysis (SFA) to estimate gender and age from synthetic face images. SFA is a versatile unsupervised learning algorithm that extracts slowly varying features from a multidimensional signal. To process very high-dimensional data, such as images, SFA can be applied hierarchically. The key idea here is to construct the training signal such that the parameters of interest, namely gender and age, vary slowly. This makes the labelling of the data implicit in the training signal and permits the use of the unsupervised algorithm in a hierarchical fashion. A simple supervised step at the very end is then sufficient to extract gender and age with high reliability. Gender was estimated with a very high accuracy, and age had an RMSE of 3.8 years for test images.
BibTeX: @inproceedings{EscalanteWiskott-2010, author = {Alberto Escalante and Laurenz Wiskott}, title = {Gender and Age Estimation from Synthetic Face Images with Hierarchical Slow Feature Analysis.}, booktitle = {International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'10), June 28--July 2, Dortmund}, year = {2010}, url = {http://www.springerlink.com/content/r031104qv7228r35} }
Escalante, A. & Wiskott, L.	2011	Heuristic Evaluation of Expansions for Non-Linear Hierarchical Slow Feature Analysis. [Abstract] [BibTeX] [URL(2)]	Proc. The 10th Intl. Conf. on Machine Learning and Applications (ICMLA'11), Dec. 18-21, Honolulu, Hawaii , 133-138. Publ. IEEE Computer Society, Los Alamitos, CA, USA.	inproceedings	Face processing with SFA (2009-now)
Abstract: Slow Feature Analysis (SFA) is a feature extraction algorithm based on the slowness principle with applications to both supervised and unsupervised learning. When implemented hierarchically, it allows for efficient processing of high-dimensional data, such as images. Expansion plays a crucial role in the implementation of non-linear SFA. In this paper, a fast heuristic method for the evaluation of expansions is proposed, consisting of tests on seven problems and two metrics. Several expansions with different complexities are evaluated. It is shown that the method allows predictions of the performance of SFA on a concrete data set, and the use of normalized expansions is justified. The proposed method is useful for the design of powerful expansions that allow the extraction of complex high-level features and provide better generalization.
BibTeX: @inproceedings{EscalanteWiskott-2011, author = {Alberto Escalante and Laurenz Wiskott}, title = {Heuristic Evaluation of Expansions for Non-Linear Hierarchical Slow Feature Analysis.}, booktitle = {Proc. The 10th Intl. Conf. on Machine Learning and Applications (ICMLA'11), Dec. 18-21, Honolulu, Hawaii}, publisher = {IEEE Computer Society}, year = {2011}, pages = {133-138}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICMLA.2011.72} }
Fellous, J.-M.; Wiskott, L.; Krüger, N. & von der Malsburg, C.	1997	Face Recognition by Elastic Bunch Graph Matching. [BibTeX]	Proc. Intl. Conf. on Vision, Recognition, Action: Neural Models of Mind and Machine .	inproceedings	Face recognition with EBGM (1993,1994)
BibTeX: @inproceedings{FellousWiskottEtAl-1997, author = {Jean-Marc Fellous and Laurenz Wiskott and Norbert Krüger and Christoph von der Malsburg}, title = {Face Recognition by Elastic Bunch Graph Matching.}, booktitle = {Proc. Intl. Conf. on Vision, Recognition, Action: Neural Models of Mind and Machine}, year = {1997} }
Franzius, M.	2008	Slowness and sparseness for unsupervised learning of spatial and object codes from naturalistic data. [Abstract] [BibTeX] [URL]	Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I .	phdthesis	SFA: Place cells (2003-2007), SFA: Learning visual invariances II (2006-2009)
Abstract: This thesis introduces a hierarchical model for unsupervised learning from naturalistic video sequences. The model is based on the principles of slowness and sparseness. Different approaches and implementations for these principles are discussed. A variety of neuron classes in the hippocampal formation of rodents and primates codes for different aspects of space surrounding the animal, including place cells, head direction cells, spatial view cells and grid cells. In the main part of this thesis, video sequences from a virtual reality environment are used for training the hierarchical model. The behavior of most known hippocampal neuron types coding for space are reproduced by this model. The type of representations generated by the model is mostly determined by the movement statistics of the simulated animal. The model approach is not limited to spatial coding. An application of the model to invariant object recognition is described, where artificial clusters of spheres or rendered fish are presented to the model. The resulting representations allow a simple extraction of the identity of the object presented as well as of its position and viewing angle.
BibTeX: @phdthesis{Franzius-2008, author = {Mathias Franzius}, title = {Slowness and sparseness for unsupervised learning of spatial and object codes from naturalistic data.}, school = {Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I}, year = {2008}, url = {http://edoc.hu-berlin.de/docviews/abstract.php?id=29124} }
Franzius, M.; Sprekeler, H. & Wiskott, L.	2007	Slowness and Sparseness Lead to Place-, Head Direction-, and Spatial-View Cells. [BibTeX]	Proc. 3rd Annual Computational Cognitive Neuroscience Conference, Nov. 1-2, San Diego, USA , III-8. Eds. Becker, S. & others	inproceedings	SFA: Place cells (2003-2007)
BibTeX: @inproceedings{FranziusSprekelerEtAl-2007e, author = {Mathias Franzius and Henning Sprekeler and Laurenz Wiskott}, title = {Slowness and Sparseness Lead to Place-, Head Direction-, and Spatial-View Cells.}, booktitle = {Proc. 3rd Annual Computational Cognitive Neuroscience Conference, Nov. 1--2, San Diego, USA}, year = {2007}, pages = {III-8} }
Franzius, M.; Sprekeler, H. & Wiskott, L.	2007	Unsupervised learning of visually driven place cells in the hippocampus. [BibTeX]	Kognitionsforschung 2007, Beiträge zur 8. Jahrestagung der Gesellschaft für Kognitionswissenschaft (KogWis'07), Mar. 19-21, Saarbrücken, Germany , 60. Eds. Frings, C.; Mecklinger, A.; Opitz, B.; Pospeschill, M.; Wentura, D. & Zimmer, H. D. Publ. Shaker Verlag, Aachen.	inproceedings	SFA: Place cells (2003-2007)
BibTeX: @inproceedings{FranziusSprekelerEtAl-2007a, author = {M. Franzius and H. Sprekeler and L. Wiskott}, title = {Unsupervised learning of visually driven place cells in the hippocampus.}, booktitle = {Kognitionsforschung 2007, Beiträge zur 8. Jahrestagung der Gesellschaft für Kognitionswissenschaft (KogWis'07), Mar. 19-21, Saarbrücken, Germany}, publisher = {Shaker Verlag}, year = {2007}, pages = {60} }
Franzius, M.; Sprekeler, H. & Wiskott, L.	2007	Unsupervised learning of place cells and head direction cells with slow feature analysis. [BibTeX]	Proc. 7th Göttingen Meeting of the German Neuroscience Society, Mar. 29 - Apr. 1, Göttingen, Germany , TS19-1C.	inproceedings	SFA: Place cells (2003-2007)
BibTeX: @inproceedings{FranziusSprekelerEtAl-2007b, author = {M. Franzius and H. Sprekeler and L. Wiskott}, title = {Unsupervised learning of place cells and head direction cells with slow feature analysis.}, booktitle = {Proc. 7th Göttingen Meeting of the German Neuroscience Society, Mar. 29 -- Apr. 1, Göttingen, Germany}, year = {2007}, pages = {TS19--1C} }
Franzius, M.; Sprekeler, H. & Wiskott, L.	2007	Learning of Place Cells, Head-Direction Cells, and Spatial-View Cells with Slow Feature Analysis on Quasi-Natural Videos. [BibTeX] [URL]	Cognitive Sciences EPrint Archive (CogPrints) , 5492.	misc	SFA: Place cells (2003-2007)
BibTeX: @misc{FranziusSprekelerEtAl-2007c, author = {Mathias Franzius and Henning Sprekeler and Laurenz Wiskott}, title = {Learning of Place Cells, Head-Direction Cells, and Spatial-View Cells with Slow Feature Analysis on Quasi-Natural Videos.}, year = {2007}, volume = {5492}, howpublished = {Cognitive Sciences EPrint Archive (CogPrints)}, url = {http://cogprints.org/5492/} }
Franzius, M.; Sprekeler, H. & Wiskott, L.	2007	Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells. [Abstract] [BibTeX] [URL] [URL(2)]	PLoS Computational Biology , 3(8), e166.	article	SFA: Place cells (2003-2007)
Abstract: We present a model for the self-organized formation of place cells, head-direction cells, and spatial-view cells in the hippocampal formation based on unsupervised learning on quasi-natural visual stimuli. The model comprises a hierarchy of Slow Feature Analysis (SFA) nodes, which were recently shown to reproduce many properties of complex cells in the early visual system [1]. The system extracts a distributed grid-like representation of position and orientation, which is transcoded into a localized place-field, head-direction, or view representation, by sparse coding. The type of cells that develops depends solely on the relevant input statistics, i.e., the movement pattern of the simulated animal. The numerical simulations are complemented by a mathematical analysis that allows us to accurately predict the output of the top SFA layer.
BibTeX: @article{FranziusSprekelerEtAl-2007d, author = {Mathias Franzius and Henning Sprekeler and Laurenz Wiskott}, title = {Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells.}, journal = {PLoS Computational Biology}, year = {2007}, volume = {3}, number = {8}, pages = {e166}, url = {http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.0030166}, doi = {http://dx.doi.org/10.1371/journal.pcbi.0030166} }
Franzius, M.; Sprekeler, H. & Wiskott, L.	2006	Slowness leads to place cells. [BibTeX]	Proc. Berlin Neuroscience Forum, June 8-10, Bad Liebenwalde, Germany , 42. Publ. Max-Delbrück-Centrum für Molekulare Medizin (MDC), Berlin.	inproceedings	SFA: Place cells (2003-2007)
BibTeX: @inproceedings{FranziusSprekelerEtAl-2006a, author = {M. Franzius and H. Sprekeler and L. Wiskott}, title = {Slowness leads to place cells.}, booktitle = {Proc. Berlin Neuroscience Forum, June 8--10, Bad Liebenwalde, Germany}, publisher = {Max-Delbrück-Centrum für Molekulare Medizin (MDC)}, year = {2006}, pages = {42} }
Franzius, M.; Sprekeler, H. & Wiskott, L.	2006	Slowness leads to place cells. [BibTeX]	Proc. 2nd Bernstein Symposium for Computational Neuroscience, Oct. 1-3, Berlin, Germany , 45. Publ. Bernstein Center for Computational Neuroscience (BCCN) Berlin.	inproceedings	SFA: Place cells (2003-2007)
BibTeX: @inproceedings{FranziusSprekelerEtAl-2006b, author = {M. Franzius and H. Sprekeler and L. Wiskott}, title = {Slowness leads to place cells.}, booktitle = {Proc. 2nd Bernstein Symposium for Computational Neuroscience, Oct. 1--3, Berlin, Germany}, publisher = {Bernstein Center for Computational Neuroscience (BCCN) Berlin}, year = {2006}, pages = {45} }
Franzius, M.; Sprekeler, H. & Wiskott, L.	2006	Slowness leads to place cells. [BibTeX]	Proc. 15th Annual Computational Neuroscience Meeting (CNS'06), July 16-20, Edinburgh, Scotland .	inproceedings	SFA: Place cells (2003-2007)
BibTeX: @inproceedings{FranziusSprekelerEtAl-2006c, author = {M. Franzius and H. Sprekeler and L. Wiskott}, title = {Slowness leads to place cells.}, booktitle = {Proc. 15th Annual Computational Neuroscience Meeting (CNS'06), July 16--20, Edinburgh, Scotland}, year = {2006} }
Franzius, M.; Vollgraf, R. & Wiskott, L.	2007	From Grids to Places. [Abstract] [BibTeX] [URL] [URL(2)]	J. Computational Neuroscience , 22(3), 297-299.	article	From Grids to Places (2005, 2006)
Abstract: Hafting et al. (2005) described grid cells in the dorsocaudal region of the medial enthorinal cortex (dMEC). These cells show a strikingly regular grid-like firing-pattern as a function of the position of a rat in an enclosure. Since the dMEC projects to the hippocampal areas containing the well-known place cells, the question arises whether and how the localized responses of the latter can emerge based on the output of grid cells. Here, we show that, starting with simulated grid-cells, a simple linear transformation maximizing sparseness leads to a localized representation similar to place fields.
BibTeX: @article{FranziusVollgrafEtAl-2007, author = {Mathias Franzius and Roland Vollgraf and Laurenz Wiskott}, title = {From Grids to Places.}, journal = {J. Computational Neuroscience}, year = {2007}, volume = {22}, number = {3}, pages = {297--299}, url = {http://www.springerlink.com/content/r6lj66670057871q/} }
Franzius, M.; Vollgraf, R. & Wiskott, L.	2006	From Grids to Places. [BibTeX] [URL]	Cognitive Sciences EPrint Archive (CogPrints) , 5101.	misc	From Grids to Places (2005, 2006)
BibTeX: @misc{FranziusVollgrafEtAl-2006, author = {Mathias Franzius and Roland Vollgraf and Laurenz Wiskott}, title = {From Grids to Places.}, year = {2006}, volume = {5101}, howpublished = {Cognitive Sciences EPrint Archive (CogPrints)}, url = {http://cogprints.org/5101/} }
Franzius, M.; Wilbert, N. & Wiskott, L.	2008	Invariant Object Recognition with Slow Feature Analysis. [BibTeX] [URL] [URL(2)]	Proc. 18th Intl. Conf. on Artificial Neural Networks (ICANN'08), Prague , Lecture Notes in Computer Science , 5163, 961-970. Eds. Kurková, V.; Neruda, R. & Koutník, J. Publ. Springer.	inproceedings	SFA: Learning visual invariances II (2006-2009)
BibTeX: @inproceedings{FranziusWilbertEtAl-2008b, author = {Mathias Franzius and Niko Wilbert and Laurenz Wiskott}, title = {Invariant Object Recognition with Slow Feature Analysis.}, booktitle = {Proc. 18th Intl. Conf. on Artificial Neural Networks (ICANN'08), Prague}, publisher = {Springer}, year = {2008}, volume = {5163}, pages = {961--970}, url = {http://dx.doi.org/10.1007/978-3-540-87536-9_98} }
Franzius, M.; Wilbert, N. & Wiskott, L.	2011	Invariant Object Recognition and Pose Estimation with Slow Feature Analysis. [Abstract] [BibTeX] [URL]	Neural Computation , 23(9), 2289-2323.	article	SFA: Learning visual invariances II (2006-2009)
Abstract: Primates are very good at recognizing objects independent of viewing angle or retinal position, and they outperform existing computer vision systems by far. But invariant object recognition is only one prerequisite for successful interaction with the environment. An animal also needs to assess an object's position and relative rotational angle. We propose here a model that is able to extract object identity, position, and rotation angles. We demonstrate the model behavior on complex three-dimensional objects under translation and rotation in depth on a homogeneous background. A similar model has previously been shown to extract hippocampal spatial codes from quasi-natural videos. The framework for mathematical analysis of this earlier application carries over to the scenario of invariant object recognition. Thus, the simulation results can be explained analytically even for the complex high-dimensional data we employed.
BibTeX: @article{FranziusWilbertEtAl-2011, author = {Franzius, Mathias and Wilbert, Niko and Wiskott, Laurenz}, title = {Invariant Object Recognition and Pose Estimation with Slow Feature Analysis.}, journal = {Neural Computation}, year = {2011}, volume = {23}, number = {9}, pages = {2289--2323}, url = {http://www.mitpressjournals.org/doi/pdf/10.1162/NECO_a_00171}, doi = {http://dx.doi.org/10.1162/NECO_a_00171} }
Franzius, M.; Wilbert, N. & Wiskott, L.	2008	Unsupervised learning of invariant 3D-object and pose representations with slow feature analysis. [BibTeX]	Proc. Federation of European Neuroscience Societies Forum (FENS'08), July 12-16, Geneva, Switzerland .	inproceedings	SFA: Learning visual invariances II (2006-2009)
BibTeX: @inproceedings{FranziusWilbertEtAl-2008a, author = {Mathias Franzius and Niko Wilbert and Laurenz Wiskott}, title = {Unsupervised learning of invariant 3D-object and pose representations with slow feature analysis.}, booktitle = {Proc. Federation of European Neuroscience Societies Forum (FENS'08), July 12-16, Geneva, Switzerland}, year = {2008} }
Franzius, M.; Wilbert, N. & Wiskott, L.	2007	Unsupervised Learning of Invariant 3D-Object Representations with Slow Feature Analysis. [BibTeX]	Proc. 3rd Bernstein Symposium for Computational Neuroscience, Sep. 24-27, Göttingen, Germany , 105. Publ. Bernstein Center for Computational Neuroscience (BCCN) Göttingen.	inproceedings	SFA: Learning visual invariances II (2006-2009)
BibTeX: @inproceedings{FranziusWilbertEtAl-2007, author = {Mathias Franzius and Niko Wilbert and Laurenz Wiskott}, title = {Unsupervised Learning of Invariant 3D-Object Representations with Slow Feature Analysis.}, booktitle = {Proc. 3rd Bernstein Symposium for Computational Neuroscience, Sep. 24--27, Göttingen, Germany}, publisher = {Bernstein Center for Computational Neuroscience (BCCN) Göttingen}, year = {2007}, pages = {105} }
Ha Quang, M.; Ha Kang, S. & Le, T.M.	2010	Image and video colorization using vector-valued reproducing kernel Hilbert spaces. [BibTeX] [URL]	Journal of Mathematical Imaging and Vision , 37(1), 49-65.	article	N.N.
BibTeX: @article{HaHaEtAl-2010, author = {Ha Quang, Minh and Ha Kang, Sung and Triet Minh Le}, title = {Image and video colorization using vector-valued reproducing kernel Hilbert spaces.}, journal = {Journal of Mathematical Imaging and Vision}, year = {2010}, volume = {37}, number = {1}, pages = {49--65}, url = {http://itb.biologie.hu-berlin.de/~minh/color_01_15_2010_final.pdf} }
Ha Quang, M.; Ha Kang, S. & Le, T.M.	2009	Reproducing kernels and colorization. [BibTeX]	Proceedings of the 8th International Conference on Sampling Theory and Applications (SAMPTA) .	inproceedings	N.N.
BibTeX: @inproceedings{HaHaEtAl-2009, author = {Ha Quang, Minh and Ha Kang, Sung and Triet Minh Le}, title = {Reproducing kernels and colorization.}, booktitle = {Proceedings of the 8th International Conference on Sampling Theory and Applications (SAMPTA)}, year = {2009} }
Ha Quang, M.; Pillonetto, G. & Chiuso, A.	2009	Nonlinear system identification via Gaussian regression and mixtures of kernels. [BibTeX]	Proceedings of the 15th IFAC Symposium on System Identification (SYSID) .	inproceedings	N.N.
BibTeX: @inproceedings{HaPillonettoEtAl-2009, author = {Ha Quang, Minh and Gianluigi Pillonetto and Alessandro Chiuso}, title = {Nonlinear system identification via Gaussian regression and mixtures of kernels.}, booktitle = {Proceedings of the 15th IFAC Symposium on System Identification (SYSID)}, year = {2009} }
Ha Quang, M. & Wiskott, L.	2011	Slow feature analysis and decorrelation filtering for separating correlated sources [Abstract] [BibTeX] [URL]	Proc. 13th International Conference on Computer Vision (ICCV), 6-13 Nov., Barcelona, Spain , 866 -873.	inproceedings
Abstract: We generalize the method of Slow Feature Analysis for vector-valued functions of multivariables and apply it to the problem of blind source separation, in particular image separation. For the linear case, exact mathematical analysis is given, which shows in particular that the sources are perfectly separated by SFA if and only if they and their first order derivatives are uncorrelated. When the sources are correlated, we apply the following technique called decorrelation filtering: use a linear filter to decorrelate the sources and their derivatives, then apply the separating matrix obtained on the filtered sources to the original sources. We show that if the filtered sources are perfectly separated by this matrix, then so are the original sources. We show how to numerically obtain such a decorrelation filter by solving a nonlinear optimization problem. This technique can also be applied to other linear separation methods, whose output signals are decorrelated, such as ICA.
BibTeX: @inproceedings{HaWiskott-2011, author = {Ha Quang, Minh and Wiskott, L.}, title = {Slow feature analysis and decorrelation filtering for separating correlated sources}, booktitle = {Proc. 13th International Conference on Computer Vision (ICCV), 6-13 Nov., Barcelona, Spain}, year = {2011}, pages = {866 -873}, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6126327&abstractAccess=no&userType=inst}, doi = {http://dx.doi.org/10.1109/ICCV.2011.6126327} }
Hinze, C.; Wilbert, N. & Wiskott, L.	2009	Visualization of higher-level receptive fields in a hierarchical model of the visual system. [Abstract] [BibTeX] [URL] [URL(3)]	Proc. 18th Annual Computational Neuroscience Meeting (CNS'09), July 18-23, Berlin, Germany .	inproceedings	Analysis of higher-level receptive fields (2007-2010)
Abstract: Early visual receptive fields have been measured extensively and are fairly well mapped. Receptive fields in higher areas, on the other hand, are very difficult to characterize, because it is not clear what they are tuned to and which stimuli to use to study them. Early visual receptive fields have been reproduced by computational models. Slow feature analysis (SFA), for instance, is an algorithm that finds functions that extract most slowly varying features from a multi-dimensional input sequence [1]. Applied to quasi-natural image sequences, i.e. image sequences derived from natural images by translation, rotation and zoom, SFA yields many properties of complex cells in V1 [2]. A hierarchical network of SFA units learns invariant object representations much like in IT [3]. These successes suggest that units of intermediate layers in the network might share properties with cells in V2 or V4. The goal of this project is therefore to develop techniques to visualize and characterize such units to understand how cells in V2/V4 might work. This is nontrivial because the units are highly nonlinear. The algorithm is gradient-based and applied in a cascade within the network. We start with a natural image patch as an input, which then gets optimized by gradient ascent to maximize the output of one particular unit. Figure 1 shows such optimal stimuli for units in the first (a, b) and the second layer (c, d). The latter can be associated with cells in V2/V4. We plan to extend this to higher layers and larger receptive fields and will also develop techniques to visualize the invariances of the units, i.e. those variations to the input that have little effect on the unit's output. The long-term goal is to provide a good stimulus set for characterizing cells in V2/V4. [Figure] Figure 1. Optimal stimuli of units in the first layer (a, b) and the second layer (c, d) of a hierarchical SFA network optimized for slowness and trained with quasi-natural image sequences. References 1. Wiskott L, Sejnowski TJ: Slow feature analysis: Unsupervised learning of invariances. Neural Computation 2002, 14:715-770. 2. Berkes P, Wiskott L: Slow feature analysis yields a rich repertoire of complex cell properties. J Vision 2005, 5:579-602. 3. Franzius M, Wilbert N, Wiskott L: Invariant object recognition with slow feature analysis. Proc 18th Int'l Conf on Artificial Neural Networks 2008, 961-970.
BibTeX: @inproceedings{HinzeWilbertEtAl-2009, author = {Christian Hinze and Niko Wilbert and Laurenz Wiskott}, title = {Visualization of higher-level receptive fields in a hierarchical model of the visual system.}, booktitle = {Proc. 18th Annual Computational Neuroscience Meeting (CNS'09), July 18--23, Berlin, Germany}, year = {2009}, url = {http://www.biomedcentral.com/1471-2202/10/S1/P158}, doi = {http://dx.doi.org/10.1186/1471-2202-10-S1-P158} }
Kempermann, G. & Wiskott, L.	2004	What is the functional role of new neurons in the adult dentate gyrus? [BibTeX] [URL(2)]	Proc. Stem Cells in the Nervous System:~Functional and Clinical Implications 2003, Jan. 20, Paris, France , Research and Perspectives in Neurosciences (Fondation Ipsen) , 57-65. Eds. Gage, F. H.; Björklund, A.; Prochiantz, A. & Christen, Y. Publ. Springer, Berlin.	inproceedings	Adult neurogenesis: Function I (2000-2003)
BibTeX: @inproceedings{KempermannWiskott-2004, author = {Gerd Kempermann and Laurenz Wiskott}, title = {What is the functional role of new neurons in the adult dentate gyrus?}, booktitle = {Proc. Stem Cells in the Nervous System:~Functional and Clinical Implications 2003, Jan. 20, Paris, France}, publisher = {Springer}, year = {2004}, pages = {57--65} }
Kempermann, G.; Wiskott, L. & Gage, F.H.	2004	Functional Significance of Adult Neurogenesis. [Abstract] [BibTeX] [URL]	Curr. Opin. Neurobiol. , 14(2), 186-191.	article	Adult neurogenesis: Function I (2000-2003)
Abstract: "Function" is the key criterion for determining whether adult neurogenesis - be it endogenous, induced, or after transplantation - is successful and has truly generated new nerve cells. Function, however, is an elusive and problematic term. A satisfying statement of function will require evaluation on the three conceptual levels of cells, networks and systems - and potentially even beyond, on the level of psychology. Neuronal development is a lengthy process, a fact that must be considered when neuronal development has to be considered when judging causes and consequences in experiments that address function and function-dependent regulation of adult neurogenesis. Nevertheless, the information that has been obtained and published so far provides ample evidence that adult-generated neurons can function and even suggests how they might contribute to cognitive processes.
BibTeX: @article{KempermannWiskottEtAl-2004, author = {Gerd Kempermann and Laurenz Wiskott and Fred H. Gage}, title = {Functional Significance of Adult Neurogenesis.}, journal = {Curr. Opin. Neurobiol.}, year = {2004}, volume = {14}, number = {2}, pages = {186--191}, url = {http://www.sciencedirect.com/science/article/pii/S0959438804000339} }
Legenstein, R.; Wilbert, N. & Wiskott, L.	2010	Reinforcement Learning on Slow Features of High-Dimensional Input Streams. [Abstract] [BibTeX] [URL] [URL(2)]	PLoS Comput Biol , 6(8), e1000894. Publ. Public Library of Science.	article	SFA and RL on visual input (2008,2009)
Abstract: Humans and animals are able to learn complex behaviors based on a massive stream of sensory information from different modalities. Early animal studies have identified learning mechanisms that are based on reward and punishment such that animals tend to avoid actions that lead to punishment whereas rewarded actions are reinforced. However, most algorithms for reward-based learning are only applicable if the dimensionality of the state-space is sufficiently small or its structure is sufficiently simple. Therefore, the question arises how the problem of learning on high-dimensional data is solved in the brain. In this article, we propose a biologically plausible generic two-stage learning system that can directly be applied to raw high-dimensional input streams. The system is composed of a hierarchical slow feature analysis (SFA) network for preprocessing and a simple neural network on top that is trained based on rewards. We demonstrate by computer simulations that this generic architecture is able to learn quite demanding reinforcement learning tasks on high-dimensional visual input streams in a time that is comparable to the time needed when an explicit highly informative low-dimensional state-space representation is given instead of the high-dimensional visual input. The learning speed of the proposed architecture in a task similar to the Morris water maze task is comparable to that found in experimental studies with rats. This study thus supports the hypothesis that slowness learning is one important unsupervised learning principle utilized in the brain to form efficient state representations for behavioral learning.
BibTeX: @article{LegensteinWilbertEtAl-2010, author = {Robert Legenstein and Niko Wilbert and Laurenz Wiskott}, title = {Reinforcement Learning on Slow Features of High-Dimensional Input Streams.}, journal = {PLoS Comput Biol}, publisher = {Public Library of Science}, year = {2010}, volume = {6}, number = {8}, pages = {e1000894}, url = {http://dx.doi.org/10.1371%2Fjournal.pcbi.1000894}, doi = {http://dx.doi.org/10.1371/journal.pcbi.1000894} }
Lezius, S.	2007	Statistik und Modellierung der Dynamik adulter hippocampaler Neurogenese bei Mäusen. [BibTeX]	Diploma thesis, Department of Mathematics and Computer Science, Ernst-Moritz-Arndt-University Greifswald, D-17487 Greifswald, Germany .	mastersthesis	Adult neurogenesis: Dynamics II (2006-now)
BibTeX: @mastersthesis{Lezius-2007, author = {Susanne Lezius}, title = {Statistik und Modellierung der Dynamik adulter hippocampaler Neurogenese bei Mäusen.}, school = {Department of Mathematics and Computer Science}, year = {2007} }
Lezius, S.; Kirste, I.; Bandt, C.; Kempermann, G. & Wiskott, L.	2009	Quantitative modeling of the dynamics of adult hippocampal neurogenesis in mice. [Abstract] [BibTeX] [URL] [URL(3)]	Proc. 18th Annual Computational Neuroscience Meeting (CNS'09), July 18-23, Berlin, Germany .	inproceedings	Adult neurogenesis: Dynamics II (2006-now)
Abstract: The hippocampus is special in that it generates new neurons throughout life. This development of new granule cells in the adult dentate gyrus is referred to as adult hippocampal neurogenesis. A kinetic model of this development has been established [1]. Therein the process of neurogenesis is composed of a sequence of different cell types. However, the exact dynamics of neuronal development in the dentate gyrus are unknown. To quantify the development we collected time-series like data. By injections of BrdU, the dividing cells were labeled and cell numbers could be counted at different time points after injection (2 h to 21 d). We determined relative numbers of BrdU-positive cells of the respective types. These numbers allow us to monitor the development of a labeled cell cohort through the different cell types over the analyzed time period. We also determined absolute cell numbers of different unlabeled populations. They do not show a time dependence, which leads to the idea of a dynamic equilibrium of cells of the different cell types on the analyzed timescale. Based on the known properties of the process and a prior model we established a detailed mathematical model containing the different developmental stages. Here we used the idea of the Leslie matrix, which is a discrete and age-structured model of population growth. The transition probabilities were found by fitting the parameters of the model to the data. We also included a simulated labeling process by which the initial cell populations are determined in a self-consistent manner based on the transition probabilities of the model. Furthermore, the effect of label dilution is included by applying a sigmoidal detectability function. The results of the model match well the data (see figure 1). The model enables us to deduce rates for division and death of cells of the different cell types as well as properties of the labeling process. Finally, based on the eigenvectors of the transition matrix we derive an estimate for the population of unlabeled cells, which matches the experimental data well without being fitted to them. [Figure] Figure 1. The comparison of the model output and the data shows good agreement for all cell types. References 1. Kempermann G, Jessberger S, Steiner B, Kronenberg G: Milestones of neuronal development in the adult hippocampus. Trends Neurosci 2004, 27:447-452.
BibTeX: @inproceedings{LeziusKirsteEtAl-2009, author = {Susanne Lezius and Imke Kirste and Christoph Bandt and Gerd Kempermann and Laurenz Wiskott}, title = {Quantitative modeling of the dynamics of adult hippocampal neurogenesis in mice.}, booktitle = {Proc. 18th Annual Computational Neuroscience Meeting (CNS'09), July 18--23, Berlin, Germany}, year = {2009}, url = {http://www.biomedcentral.com/1471-2202/10/S1/P335}, doi = {http://dx.doi.org/10.1186/1471-2202-10-S1-P335} }
Maeyer, L.D.; Nicola, A.D.; Maetche, R.; von der Malsburg, C. & Wiskott, L.	1989	An Experimental Multiprocessor System for Distributed Parallel Computations. [Abstract] [BibTeX]	Microprocessing and Microprogramming , 26, 305-317.	article	Multiprocessor system (1986-1988)
Abstract: The availability of low-cost microprocessor chips with efficient instruction sets for specific numerical tasks (signal processors) has been exploited for building a versatile multiprocessor system, consisting of a host minicomputer augmented by a number of joint processors. The host provides a multiuser-multitasking environment and manages system resources and task scheduling. User applications can call upon one or more joint processors for parallel execution of adequately partitioned, computationally intensive numeric operations. Each joint processor has sufficient local memory for storing procedures and data and has access to regions in host memory for shared data. Kernel processes in the host and in the joint processors provide the necessary mechanism for initialization and synchronization of the distributed parallel execution of procedures.
BibTeX: @article{MaeyerNicolaEtAl-1989, author = {L. De Maeyer and A. Di Nicola and R. Maetche and Christoph von der Malsburg and Laurenz Wiskott}, title = {An Experimental Multiprocessor System for Distributed Parallel Computations.}, journal = {Microprocessing and Microprogramming}, year = {1989}, volume = {26}, pages = {305--317} }
Pillonetto, G.; Ha Quang, M. & Chiuso, A.	2011	A New Kernel-based Approach for Nonlinear System Identification [BibTeX]	IEEE Transactions on Automatic Control , 56(12).	article
BibTeX: @article{PillonettoHaEtAl-2011, author = {Gianluigi Pillonetto and Ha Quang, Minh and Alessandro Chiuso}, title = {A New Kernel-based Approach for Nonlinear System Identification}, journal = {IEEE Transactions on Automatic Control}, year = {2011}, volume = {56}, number = {12} }
Pötzsch, M.; Maurer, T.; Wiskott, L. & von der Malsburg, C.	1996	Reconstruction from Graphs Labeled with Responses of Gabor Filters. [Abstract] [BibTeX] [URL(2)]	Proc. Intl. Conf. on Artificial Neural Networks (ICANN'96), Bochum, Germany , 845-850. Eds. von der Malsburg, C.; von Seelen, W.; Vorbrüggen, J. C. & Sendhoff, B. Publ. Springer-Verlag.	inproceedings	Reconstruction from Gabor wavelets (1993-1995)
Abstract: The work presented is part of a larger effort to build a general object recognition system. Objects as well as human faces are represented by graphs labeled with Gabor filter responses. We describe an optimal method to reconstruct images from such graphs. Two examples of how this can be used to analyze the object representation or to compensate for its deficiencies are presented. Since the reconstruction method is formulated generally for an arbitray set of linear filters, it can also be applied to data produced by other systems, artificial or biological.
BibTeX: @inproceedings{PotzschMaurerEtAl-1996, author = {Michael Pötzsch and Thomas Maurer and Laurenz Wiskott and Christoph von der Malsburg}, title = {Reconstruction from Graphs Labeled with Responses of Gabor Filters.}, booktitle = {Proc. Intl. Conf. on Artificial Neural Networks (ICANN'96), Bochum, Germany}, publisher = {Springer-Verlag}, year = {1996}, pages = {845--850} }
Rasch, M.	2003	Modellierung adulter Neurogenese im Hippocampus [Modeling adult neurogenesis in the hippocampus]. [BibTeX] [URL(2)]	Diploma thesis, Institute for Biology, Humboldt University Berlin, D-10115 Berlin, Germany .	mastersthesis	Adult neurogenesis: Function I (2000-2003)
BibTeX: @mastersthesis{Rasch-2003, author = {Malte Rasch}, title = {Modellierung adulter Neurogenese im Hippocampus [Modeling adult neurogenesis in the hippocampus].}, school = {Institute for Biology}, year = {2003} }
Sprekeler, H.	2009	Slowness learning. [Abstract] [BibTeX] [URL]	Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I .	phdthesis	SFA and STDP (2003-2006), SFA: Place cells (2003-2007), SFA: Theory of complex cells (2004-2007), Extended slow feature analysis (xSFA) (2006-2008)
Abstract: In this thesis, we investigate slowness as an unsupervised learning principle of sensory processing. Two aspects are given particular emphasis: (a) the mathematical analysis of Slow Feature Analysis (SFA) as one particular implementation of slowness learning and (b) the question, how slowness learning can be implemented in a biologically plausible fashion. In the first part of the thesis, we develop a mathematical framework for SFA and show that the optimal functions for SFA are the solutions of a partial differential eigenvalue problem. The theory allows (a) to make analytical predictions for the behavior of complicated applications and (b) an intuitive understanding of how the statistics of the input data are reflected in the optimal functions of SFA. The theory is applied to the learning of place and head-direction representations and to the learning of complex cell receptive fields as found in primary visual cortex. As a technical application, we use the theoretical results to develop and test a new algorithm for nonlinear blind source separation. The first part of the thesis is concluded by an information-theoretic analysis of the relation between slowness learning and predictive coding. In the second part of the thesis, we study the question, how slowness learning could be implemented in a biologically plausible manner. To this end, we first show that spike timing-dependent plasticity can under certain conditions be interpreted as an implementation of slowness learning. Finally, we show that both gradient-based slowness learning and spike timing-dependent plasticity lead to receptive field dynamics that can be described in terms of reaction-diffusion equations.
BibTeX: @phdthesis{Sprekeler-2009, author = {Henning Sprekeler}, title = {Slowness learning.}, school = {Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I}, year = {2009}, url = {http://edoc.hu-berlin.de/docviews/abstract.php?id=29695} }
Sprekeler, H.; Michaelis, C. & Wiskott, L.	2007	Slowness: an objective for spike timing-dependent plasticity? [BibTeX]	Proc. 7th Göttingen Meeting of the German Neuroscience Society, Mar. 29 - Apr. 1, Göttingen, Germany , T27-3A.	inproceedings	SFA and STDP (2003-2006)
BibTeX: @inproceedings{SprekelerMichaelisEtAl-2007a, author = {Henning Sprekeler and Christian Michaelis and Laurenz Wiskott}, title = {Slowness: an objective for spike timing-dependent plasticity?}, booktitle = {Proc. 7th Göttingen Meeting of the German Neuroscience Society, Mar. 29 -- Apr. 1, Göttingen, Germany}, year = {2007}, pages = {T27--3A} }
Sprekeler, H.; Michaelis, C. & Wiskott, L.	2007	Slowness: An Objective for Spike-Timing-Dependent Plasticity? [Abstract] [BibTeX] [URL] [URL(2)]	PLoS Computational Biology , 3(6), e112.	article	SFA and STDP (2003-2006)
Abstract: Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here, we study the question of how this "temporal stability" or "slowness" approach can be implemented within the limits of biologically realistic spike-based learning rules. We first show that slow feature analysis, an algorithm that is based on slowness, can be implemented in linear continuous model neurons by means of a modified Hebbian learning rule. This approach provides a link to the trace rule, which is another implementation of slowness learning. Then, we show analytically that for linear Poisson neurons, slowness learning can be implemented by spike-timing-dependent plasticity (STDP) with a specific learning window. By studying the learning dynamics of STDP, we show that for functional interpretations of STDP, it is not the learning window alone that is relevant but rather the convolution of the learning window with the postsynaptic potential. We then derive STDP learning windows that implement slow feature analysis and the "trace rule." The resulting learning windows are compatible with physiological data both in shape and timescale. Moreover, our analysis shows that the learning window can be split into two functionally different components that are sensitive to reversible and irreversible aspects of the input statistics, respectively. The theory indicates that irreversible input statistics are not in favor of stable weight distributions but may generate oscillatory weight dynamics. Our analysis offers a novel interpretation for the functional role of STDP in physiological neurons.
BibTeX: @article{SprekelerMichaelisEtAl-2007b, author = {Henning Sprekeler and Christian Michaelis and Laurenz Wiskott}, title = {Slowness: An Objective for Spike-Timing--Dependent Plasticity?}, journal = {PLoS Computational Biology}, year = {2007}, volume = {3}, number = {6}, pages = {e112}, url = {http://dx.doi.org/10.1371/journal.pcbi.0030112}, doi = {http://dx.doi.org/10.1371/journal.pcbi.0030112} }
Sprekeler, H.; Michaelis, C. & Wiskott, L.	2006	Slowness: An Objective for Spike-Timing Dependent Plasticity? [BibTeX]	Proc. 2nd Bernstein Symposium for Computational Neuroscience, Oct. 1-3, Berlin, Germany , 24. Publ. Bernstein Center for Computational Neuroscience (BCCN) Berlin.	inproceedings	SFA and STDP (2003-2006)
BibTeX: @inproceedings{SprekelerMichaelisEtAl-2006a, author = {Henning Sprekeler and Christian Michaelis and Laurenz Wiskott}, title = {Slowness: An Objective for Spike-Timing Dependent Plasticity?}, booktitle = {Proc. 2nd Bernstein Symposium for Computational Neuroscience, Oct. 1--3, Berlin, Germany}, publisher = {Bernstein Center for Computational Neuroscience (BCCN) Berlin}, year = {2006}, pages = {24} }
Sprekeler, H.; Michaelis, C. & Wiskott, L.	2006	Slowness: An Objective for Spike-Timing-Dependent Plasticity? [BibTeX] [URL]	Cognitive Sciences EPrint Archive (CogPrints) , 5281.	misc	SFA and STDP (2003-2006)
BibTeX: @misc{SprekelerMichaelisEtAl-2006b, author = {Henning Sprekeler and Christian Michaelis and Laurenz Wiskott}, title = {Slowness: An Objective for Spike-Timing-Dependent Plasticity?}, year = {2006}, volume = {5281}, howpublished = {Cognitive Sciences EPrint Archive (CogPrints)}, url = {http://cogprints.org/5281/} }
Sprekeler, H. & Wiskott, D.L.	2008	Understanding Slow Feature Analysis: A Mathematical Framework [Abstract] [BibTeX] [URL]	.	misc
Abstract: Slow feature analysis is an algorithm for unsupervised learning of invariant representations from data with temporal correlations. Here, we present a mathematical analysis of slow feature analysis for the case where the input-output functions are not restricted in complexity. We show that the optimal functions obey a partial differential eigenvalue problem of a type that is common in theoretical physics. This analogy allows the transfer of mathematical techniques and intuitions from physics to concrete applications of slow feature analysis, thereby providing the means for analytical predictions and a better understanding of simulation results. We put particular emphasis on the situation where the input data are generated from a set of statistically independent sources. The dependence of the optimal functions on the sources is calculated analytically for the cases where the sources have Gaussian or uniform distribution.
BibTeX: @misc{SprekelerWiskott-2008, author = {Henning Sprekeler and Dr. Laurenz Wiskott}, title = {Understanding Slow Feature Analysis: A Mathematical Framework}, year = {2008}, url = {http://cogprints.org/6223/} }
Sprekeler, H. & Wiskott, L.	2011	A Theory of Slow Feature Analysis for Transformation-Based Input Signals with an Application to Complex Cells. [Abstract] [BibTeX] [URL] [URL(2)]	Neural Computation , 23(2), 303-335.	article	SFA: Theory of complex cells (2004-2007)
Abstract: We develop a group theoretical analysis of slow feature analysis for the case where the input data are generated by applying a set of continuous transformations to static templates. As an application of the theory, we analytically derive nonlinear visual receptive fields and show that their optimal stimuli as well as the orientation and frequency tuning are in good agreement with previous simulations of complex cells in primary visual cortex (Berkes and Wiskott, 2005). The theory suggests that side- and end-stopping can be interpreted as a weak breaking of translation invariance. Direction selectivity is also discussed.
BibTeX: @article{SprekelerWiskott-2011, author = {Henning Sprekeler and Laurenz Wiskott}, title = {A Theory of Slow Feature Analysis for Transformation-Based Input Signals with an Application to Complex Cells.}, journal = {Neural Computation}, year = {2011}, volume = {23}, number = {2}, pages = {303--335}, url = {http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00072}, doi = {http://dx.doi.org/10.1162/NECO_a_00072} }
Sprekeler, H. & Wiskott, L.	2007	Spike-timing-dependent plasticity and temporal input statistics. [BibTeX] [URL]	Proc. 16th Annual Computational Neuroscience Meeting (CNS'06), July 7-12, Toronto, Canada .	inproceedings	SFA and STDP (2003-2006)
BibTeX: @inproceedings{SprekelerWiskott-2007, author = {H. Sprekeler and L. Wiskott}, title = {Spike-timing-dependent plasticity and temporal input statistics.}, booktitle = {Proc. 16th Annual Computational Neuroscience Meeting (CNS'06), July 7--12, Toronto, Canada}, year = {2007}, url = {http://www.biomedcentral.com/1471-2202/8/S2/P86}, doi = {http://dx.doi.org/10.1186/1471-2202/8/S2/P86} }
Sprekeler, H. & Wiskott, L.	2006	Analytical derivation of complex cell properties from the slowness principle. [BibTeX]	Proc. Berlin Neuroscience Forum, June 8-10, Bad Liebenwalde, Germany , 65-66. Publ. Max-Delbrück-Centrum für Molekulare Medizin (MDC), Berlin.	inproceedings	SFA: Theory of complex cells (2004-2007)
BibTeX: @inproceedings{SprekelerWiskott-2006a, author = {H. Sprekeler and L. Wiskott}, title = {Analytical derivation of complex cell properties from the slowness principle.}, booktitle = {Proc. Berlin Neuroscience Forum, June 8--10, Bad Liebenwalde, Germany}, publisher = {Max-Delbrück-Centrum für Molekulare Medizin (MDC)}, year = {2006}, pages = {65--66} }
Sprekeler, H. & Wiskott, L.	2006	Analytical Derivation of Complex Cell Properties from the Slowness Principle. [BibTeX]	Proc. 15th Annual Computational Neuroscience Meeting (CNS'06), July 16-20, Edinburgh, Scotland .	inproceedings	SFA: Theory of complex cells (2004-2007)
BibTeX: @inproceedings{SprekelerWiskott-2006b, author = {Sprekeler, Henning and Wiskott, Laurenz}, title = {Analytical Derivation of Complex Cell Properties from the Slowness Principle.}, booktitle = {Proc. 15th Annual Computational Neuroscience Meeting (CNS'06), July 16--20, Edinburgh, Scotland}, year = {2006} }
Sprekeler, H. & Wiskott, L.	2006	Analytical Derivation of Complex Cell Properties from the Slowness Principle. [BibTeX]	Proc. Conference on Mathematical Neuroscience (NEUROMATH 06), Sep. 1-4, Andorra , 62.	inproceedings	SFA: Theory of complex cells (2004-2007)
BibTeX: @inproceedings{SprekelerWiskott-2006c, author = {Henning Sprekeler and Laurenz Wiskott}, title = {Analytical Derivation of Complex Cell Properties from the Slowness Principle.}, booktitle = {Proc. Conference on Mathematical Neuroscience (NEUROMATH 06), Sep. 1-4, Andorra}, year = {2006}, pages = {62} }
Sprekeler, H. & Wiskott, L.	2006	Analytical Derivation of Complex Cell Properties from the Slowness Principle. [BibTeX]	Proc. 2nd Bernstein Symposium for Computational Neuroscience, Oct. 1-3, Berlin, Germany , 67. Publ. Bernstein Center for Computational Neuroscience (BCCN) Berlin.	inproceedings	SFA: Theory of complex cells (2004-2007)
BibTeX: @inproceedings{SprekelerWiskott-2006d, author = {Henning Sprekeler and Laurenz Wiskott}, title = {Analytical Derivation of Complex Cell Properties from the Slowness Principle.}, booktitle = {Proc. 2nd Bernstein Symposium for Computational Neuroscience, Oct. 1--3, Berlin, Germany}, publisher = {Bernstein Center for Computational Neuroscience (BCCN) Berlin}, year = {2006}, pages = {67} }
Sprekeler, H.; Zito, T. & Wiskott, L.	2010	An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation. [Abstract] [BibTeX] [URL] [URL(2)]	Cognitive Sciences EPrint Archive (CogPrints) , 7056.	misc	Extended slow feature analysis (xSFA) (2006-2008)
Abstract: We present and test an extension of slow feature analysis as a novel approach to nonlinear blind source separation. The algorithm relies on temporal correlations and iteratively reconstructs a set of statistically independent sources from arbitrary nonlinear instantaneous mixtures. Simulations show that it is able to invert a complicated nonlinear mixture of two audio signals with a reliability of more than 90%. The algorithm is based on a mathematical analysis of slow feature analysis for the case of input data that are generated from statistically independent sources.
BibTeX: @misc{SprekelerZitoEtAl-2010, author = {Henning Sprekeler and Tiziano Zito and Laurenz Wiskott}, title = {An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation.}, year = {2010}, volume = {7056}, howpublished = {Cognitive Sciences EPrint Archive (CogPrints)}, url = {http://cogprints.org/7056/} }
Wicklein, M.; Strausfeld, N.J.; Sejnowski, T.; Sabes, P. & Wiskott, L.	1998	Looming sensitivity in hummingbird hawkmoths: Neurons and models. [Abstract] [BibTeX]	Proc. Society for Neuroscience 28th Annual Meeting , 24, 188.	inproceedings	N.N.
Abstract: Intracellular recordings in Manduca sexta (Sphingidae, Lepidoptera) identified a class of wide-field neuron that responded selectively to looming or receding stimuli. Clockwise and counter-clockwise rotating spirals and expanding or contracting discs simulated looming and anti-looming. Both spirals and discs provide the eye with outwardly or inwardly moving edges, while the spiral simulates looming or anti-looming maintaining a constant area, perimeter length, and luminance on the retina. Type 1 cells are activated only by the disc and not the spiral, effectively distinguishing expansion from contraction by measuring perimeter length. The cell class is futher divided: Type 1a neurons responded to looming and were inhibited by image size decrease (anti-looming) whereas type 1b neurons were activated by anti-looming and inhibited by looming. The proposed model for the type 1 neurons requires them to be mutually inhibited, while being fed by two systems of retinotopically organized directional insensitive and motion sensitive edge-detectors through an intermediate level of elements that either preserve or invert the signal. The first of the two systems provides an excitatory output on the type 1a neuron, the latter inhibits the type 1b neuron. As the edge of the looming stimulus expands on the retina there is a recruitment of sequentially stimulated edge detectors increasing the depolarization of the class 1a cell and increasing the inhibition of the class 1b cell. The opposite occurs with reversed stimulus: excitation of the class 1a cell diminishes, while the class 1b cell is gradually released from inhibition. Reciprocal inhibition occurs between the class 1a and class 1b neurons to provide the observed responses of excitation to looming and inhibition to anti-looming or the reverse. We implemented the proposed model camparing the performance of the real curcuit with the model. The model proves to be able to simulate the essential features of the neuronal circuit.
BibTeX: @inproceedings{WickleinStrausfeldEtAl-1998, author = {Martina Wicklein and N. J. Strausfeld and Terrence Sejnowski and P. Sabes and Laurenz Wiskott}, title = {Looming sensitivity in hummingbird hawkmoths: Neurons and models.}, booktitle = {Proc. Society for Neuroscience 28th Annual Meeting}, year = {1998}, volume = {24}, pages = {188} }
Wilbert, N.; Franzius, M.; Cichy, R.; Schmidt, S.; Brandt, S. & Wiskott, L.	2007	Towards a model of visual attention. [BibTeX]	Proc. Midterm Evaluation of the German National Network for Computational Neuroscience, Dec. 3-4, Berlin, Germany , 30.	inproceedings	SFA: Learning visual invariances II (2006-2009)
BibTeX: @inproceedings{WilbertFranziusEtAl-2007, author = {Niko Wilbert and Mathias Franzius and Radoslaw Cichy and Sein Schmidt and Stephan Brandt and Laurenz Wiskott}, title = {Towards a model of visual attention.}, booktitle = {Proc. Midterm Evaluation of the German National Network for Computational Neuroscience, Dec. 3--4, Berlin, Germany}, year = {2007}, pages = {30} }
Wilbert, N.; Legenstein, R.; Franzius, M. & Wiskott, L.	2009	Reinforcement learning on complex visual stimuli. [BibTeX] [URL]	Proc. 18th Annual Computational Neuroscience Meeting (CNS'09), July 18-23, Berlin, Germany .	inproceedings	SFA and RL on visual input (2008,2009)
BibTeX: @inproceedings{WilbertLegensteinEtAl-2009, author = {Niko Wilbert and Robert Legenstein and Mathias Franzius and Laurenz Wiskott}, title = {Reinforcement learning on complex visual stimuli.}, booktitle = {Proc. 18th Annual Computational Neuroscience Meeting (CNS'09), July 18--23, Berlin, Germany}, year = {2009}, url = {http://www.biomedcentral.com/1471-2202/10/S1/P90}, doi = {http://dx.doi.org/10.1186/1471-2202-10-S1-P90} }
Wilbert, N. & Wiskott, L.	2010	Hierarchical Slow Feature Analysis and Top-Down Processes. [Abstract] [BibTeX] [URL] [URL(3)]	Proc. Bernstein Conference on Computational Neuroscience, Sep 27-Oct 1, Berlin, Germany .	inproceedings	N.N.
Abstract: Top-down processes are thought to play an important role in the mammalian visual system, e.g., for interpreting ambiguous stimuli. Slow Feature Analysis (SFA) [2] on the other hand is proven to be an efficient algorithm for the bottom-up processing of visual stimuli [2][3]. Therefore it seems natural to combine bottom-up SFA with top-down processes. SFA is an unsupervised learning algorithm that leverages the time structure of incoming stimuli to extract higher-level features. The SFA algorithm works with continuous, real variables. The algorithm itself is linear, but can be combined with a prior expansion into a more powerful function space. Quadratic polynomials have been used successfully in hierarchical networks for the extraction of high-level features from complex visual stimuli. Unfortunately this expansion makes it difficult to relate input and output components in the layers. In particular it is generally not possible to invert the bottom-up mapping, which indicates serious obstacles for top-down processes. We explored techniques to address this inversion problem. Our methods combine gradient decent and vector quantization algorithms and allowed stimulus reconstruction at the lowest layer (see Fig. 1). The results also suggest that a further increase in reconstruction performance will require a different expansion that is partly optimized for the top-down step. [Figure] Figure 1. Stimulus reconstruction from higher-level features. (a) shows the reconstruction for a single receptive field patch on the lowest layer, with complex cell like output behavior. On the left is the original stimulus, on the right side the reconstruction, which was calculated from the layer output. In (b) the same reconstruction technique has been applied to a whole image. The first picture is the original stimulus, the second one is the reconstruction from the lowest layer output. The third image is the reconstruction from the second layer output, showing some significant reconstruction errors. References 1. Wiskott L, Sejnowski TJ: Slow feature analysis: Unsupervised learning of invariances. Neural Computation 2002; 14(4):715-770. 2. Franzius M, Sprekeler H, and Wiskott L: Slowness and sparseness lead to place, head-diretion and spatial-view cells. Public Library of Science (PLoS) Computational Biology, 3(8):e166, 2007. 3. Franzius M, Wilbert N, and Wiskott L: Invariant Object Recognition with Slow Feature Analysis. Proc. 18th Int'l Conf. on Artificial Neural Networks, ICANN'08, Prague, Sep. 3-6, eds. Vera Kurková and Roman Neruda and Jan Koutník, publ. Springer-Verlag, pp. 961-970.
BibTeX: @inproceedings{WilbertWiskott-2010, author = {N. Wilbert and L. Wiskott}, title = {Hierarchical Slow Feature Analysis and Top-Down Processes.}, booktitle = {Proc. Bernstein Conference on Computational Neuroscience, Sep 27--Oct 1, Berlin, Germany}, year = {2010}, url = {http://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.3389/conf.fncom.2010.51.00119}, doi = {http://dx.doi.org/10.3389/conf.fncom.2010.51.00119} }
Wilbert, N.; Zito, T.; Schuppner, R.-B.; Zbigniew J e.-S.; Wiskott, L. & Berkes, P.	2011	Building extensible frameworks for data processing: The case of MDP, Modular toolkit for Data Processing. [Abstract] [BibTeX] [URL]	Journal of Computational Science .	article	MDP: Modular toolkit for data processing (2003-now)
Abstract: Data processing is a ubiquitous task in scientific research, and much energy is spent on the development of appropriate algorithms. It is thus relatively easy to find software implementations of the most common methods. On the other hand, when building concrete applications, developers are often confronted with several additional chores that need to be carried out beside the individual processing steps. These include for example training and executing a sequence of several algorithms, writing code that can be executed in parallel on several processors, or producing a visual description of the application. The Modular toolkit for Data Processing (MDP) is an open source Python library that provides an implementation of several widespread algorithms and offers a unified framework to combine them to build more complex data processing architectures. In this paper we concentrate on some of the newer features of MDP, focusing on the choices made to automatize repetitive tasks for users and developers. In particular, we describe the support for parallel computing and how this is implemented via a flexible extension mechanism. We also briefly discuss the support for algorithms that require bi-directional data flow.
BibTeX: @article{WilbertZitoEtAl-2011, author = {Niko Wilbert and Tiziano Zito and Rike-Benjamin Schuppner and Zbigniew Jedrzejewski-Szmek and Laurenz Wiskott and Pietro Berkes}, title = {Building extensible frameworks for data processing: The case of MDP, Modular toolkit for Data Processing.}, journal = {Journal of Computational Science}, year = {2011}, url = {http://www.sciencedirect.com/science/article/pii/S1877750311000913}, doi = {http://dx.doi.org/10.1016/j.jocs.2011.10.005} }
Wiskott, L.	2006	How Does Our Visual System Achieve Shift and Size Invariance? [BibTeX]	Chapter 16 in 23 Problems in Systems Neuroscience , 322-340. Eds. van Hemmen, J. L. & Sejnowski, T. J. Publ. Oxford University Press, New York.	incollection	Visual invariances: a review (2000)
BibTeX: @incollection{Wiskott-2006b, author = {Laurenz Wiskott}, title = {How Does Our Visual System Achieve Shift and Size Invariance?}, booktitle = {23 Problems in Systems Neuroscience}, publisher = {Oxford University Press}, year = {2006}, pages = {322--340} }
Wiskott, L.	2001	Some Ideas About Organic Computing. [Abstract] [BibTeX] [URL(2)]	Preproc. Organic Computing: Towards Structured Design of Processes, Nov. 23-24, Paderborn, Germany , 39-42. Eds. Herzel, H.; von der Malsburg, C.; von Seelen, W. & Würtz, R. P.	inproceedings	N.N.
Abstract: Well, I have been thinking about a possible position statement for the symposium on organic computing for quite some time now, but I still feel that I cannot provide any particularly qualified text. So be warned that this is a rather naive statement, in the sense that I have no experience in hardware issues of organic computing and that I did not take the time to educate myself by reading related papers. I have some experience in neuroinformatics, but I don't feel like speculating on that, since, as far as I understand, the focus of the symposium is on combining and communicating between these fields rather than pushing forward any single discipline. I also feel that ethical issues should be raised, although I have not much experience on that either. So here are some unqualified speculations and fragments on hardware-software and ethical issues of organic computing in the (partly rather far) future. Maybe they will induce some qualified thoughts on the reader's side.
BibTeX: @inproceedings{Wiskott-2001b, author = {Laurenz Wiskott}, title = {Some Ideas About Organic Computing.}, booktitle = {Preproc. Organic Computing: Towards Structured Design of Processes, Nov. 23-24, Paderborn, Germany}, year = {2001}, pages = {39--42} }
Wiskott, L.	2001	Unsupervised Learning of Invariances in a Simple Model of the Visual System. [BibTeX]	Proc. The Mathematical, Computational and Biological Study of Vision, Nov. 4-10, Oberwolfach , 21-22. Eds. Mumford, D.; Morel, J.-M. & von der Malsburg, C. Publ. Mathematisches Forschungsinstitut Oberwolfach.	inproceedings	SFA: Learning visual invariances I (1997-1999)
BibTeX: @inproceedings{Wiskott-2001a, author = {Laurenz Wiskott}, title = {Unsupervised Learning of Invariances in a Simple Model of the Visual System.}, booktitle = {Proc. The Mathematical, Computational and Biological Study of Vision, Nov. 4--10, Oberwolfach}, publisher = {Mathematisches Forschungsinstitut Oberwolfach}, year = {2001}, pages = {21--22} }
Wiskott, L.	1998	Learning Invariance Manifolds. [BibTeX]	Proc. 8th Intl. Conf. on Artificial Neural Networks (ICANN'98), Skövde, Sweden , Perspectives in Neural Computing , 555-560. Eds. Niklasson, L.; Bodén, M. & Ziemke, T. Publ. Springer, London.	inproceedings	SFA: Learning visual invariances I (1997-1999)
BibTeX: @inproceedings{Wiskott-1998b, author = {Laurenz Wiskott}, title = {Learning Invariance Manifolds.}, booktitle = {Proc. 8th Intl. Conf. on Artificial Neural Networks (ICANN'98), Skövde, Sweden}, publisher = {Springer}, year = {1998}, pages = {555--560} }
Wiskott, L.	1999	Unsupervised Learning and Generalization of Translation Invariance in a Simple Model of the Visual System. [BibTeX]	Learning and Adaptivity for Connectionist Models and Neural Networks, Proc. Meeting of the GI-Working Group 1.1.2 ``Connectionism'', Sep. 29, Magdeburg, Germany , 56-67. Ed. Paaß, G. Publ. GMD-Forschungszentrum Informationstechnik GmbH, Sankt Augustin.	inproceedings	SFA: Learning visual invariances I (1997-1999)
BibTeX: @inproceedings{Wiskott-1999c, author = {Laurenz Wiskott}, title = {Unsupervised Learning and Generalization of Translation Invariance in a Simple Model of the Visual System.}, booktitle = {Learning and Adaptivity for Connectionist Models and Neural Networks, Proc. Meeting of the GI-Working Group 1.1.2 ``Connectionism'', Sep. 29, Magdeburg, Germany}, publisher = {GMD-Forschungszentrum Informationstechnik GmbH}, year = {1999}, pages = {56--67} }
Wiskott, L.	1997	Segmentation from Motion: Combining Gabor- and Mallat-Wavelets to Overcome Aperture and Correspondence Problem. [BibTeX]	Proc. 7th Intl. Conf. on Computer Analysis of Images and Patterns (CAIP'97), Kiel, Germany , Lecture Notes in Computer Science (1296), 329-336. Eds. Sommer, G.; Daniilidis, K. & Pauli, J. Publ. Springer-Verlag, Heidelberg.	inproceedings	Segmentation from motion (1993)
BibTeX: @inproceedings{Wiskott-1997b, author = {Laurenz Wiskott}, title = {Segmentation from Motion: Combining Gabor- and Mallat-Wavelets to Overcome Aperture and Correspondence Problem.}, booktitle = {Proc. 7th Intl. Conf. on Computer Analysis of Images and Patterns (CAIP'97), Kiel, Germany}, publisher = {Springer-Verlag}, year = {1997}, number = {1296}, pages = {329--336} }
Wiskott, L.	1997	Phantom Faces for Face Analysis. [BibTeX]	Proc. 7th Intl. Conf. on Computer Analysis of Images and Patterns (CAIP'97), Kiel, Germany , Lecture Notes in Computer Science (1296), 480-487. Eds. Sommer, G.; Daniilidis, K. & Pauli, J. Publ. Springer-Verlag, Heidelberg.	inproceedings	Face analysis with EBGM (1993-1995)
BibTeX: @inproceedings{Wiskott-1997c, author = {Laurenz Wiskott}, title = {Phantom Faces for Face Analysis.}, booktitle = {Proc. 7th Intl. Conf. on Computer Analysis of Images and Patterns (CAIP'97), Kiel, Germany}, publisher = {Springer-Verlag}, year = {1997}, number = {1296}, pages = {480--487} }
Wiskott, L.	2006	Is Slowness a Learning Principle of Visual Cortex? [BibTeX]	Proc. Japan-Germany Symposium on Computational Neuroscience, Feb. 1-4, Wako, Saitama, Japan , 25. Publ. RIKEN Brain Science Institute.	inproceedings	SFA: Learning visual invariances I (1997-1999), SFA: Complex cells (2001-2003)
BibTeX: @inproceedings{Wiskott-2006a, author = {Laurenz Wiskott}, title = {Is Slowness a Learning Principle of Visual Cortex?}, booktitle = {Proc. Japan-Germany Symposium on Computational Neuroscience, Feb. 1-4, Wako, Saitama, Japan}, publisher = {RIKEN Brain Science Institute}, year = {2006}, pages = {25} }
Wiskott, L.	2003	Slow Feature Analysis: A Theoretical Analysis of Optimal Free Responses. [Abstract] [BibTeX] [URL] [URL(2)]	Neural Computation , 15(9), 2147-2177.	article	SFA: Theory of free responses (1998-2002)
Abstract: Temporal slowness is a learning principle that allows learning of invariant representations by extracting slowly varying features from quickly varying input signals. Slow feature analysis (SFA) is an efficient algorithm based on this principle, which has been applied to the learning of translation, scale, and other invariances in a simple model of the visual system. Here a theoretical analysis of the optimization problem solved by SFA is presented, which provides a deeper understanding of the simulation results obtained in previous studies.
BibTeX: @article{Wiskott-2003a, author = {Wiskott, Laurenz}, title = {Slow Feature Analysis: A Theoretical Analysis of Optimal Free Responses.}, journal = {Neural Computation}, year = {2003}, volume = {15}, number = {9}, pages = {2147--2177}, url = {http://www.mitpressjournals.org/doi/abs/10.1162/089976603322297331}, doi = {http://dx.doi.org/10.1162/089976603322297331} }
Wiskott, L.	2003	Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis. [Abstract] [BibTeX] [URL] [URL(2)]	arXiv.org e-Print archive , 0312317.	misc	SFA: Estimating driving forces (2000-2003)
Abstract: Slow feature analysis (SFA) is a new technique for extracting slowly varying features from a quickly varying signal. It is shown here that SFA can be applied to nonstationary time series to estimate a single underlying driving force with high accuracy up to a constant offset and a factor. Examples with a tent map and a logistic map illustrate the performance.
BibTeX: @misc{Wiskott-2003b, author = {Laurenz Wiskott}, title = {Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis.}, year = {2003}, volume = {0312317}, howpublished = {arXiv.org e-Print archive}, url = {http://arxiv.org/abs/cond-mat/0312317/} }
Wiskott, L.	2003	How Does Our Visual System Achieve Shift and Size Invariance? [Abstract] [BibTeX] [URL] [URL(2)]	Cognitive Sciences EPrint Archive (CogPrints) , 3321.	misc	Visual invariances: a review (2000)
Abstract: The question of shift and size invariance in the primate visual system is discussed. After a short review of the relevant neurobiology and psychophysics, a more detailed analysis of computational models is given. The two main types of networks considered are the dynamic routing circuit model and invariant feature networks, such as the neocognitron. Some specific open questions in context of these models are raised and possible solutions discussed.
BibTeX: @misc{Wiskott-2003c, author = {Laurenz Wiskott}, title = {How Does Our Visual System Achieve Shift and Size Invariance?}, year = {2003}, volume = {3321}, howpublished = {Cognitive Sciences EPrint Archive (CogPrints)}, url = {http://cogprints.org/3321/} }
Wiskott, L.	2000	Unsupervised Learning of Invariances in a Simple Model of the Visual System. [BibTeX]	Proc. 9th Annual Computational Neuroscience Meeting (CNS'00), July 16-20, Brugge, Belgium , 157.	inproceedings	SFA: Learning visual invariances I (1997-1999)
BibTeX: @inproceedings{Wiskott-2000, author = {Laurenz Wiskott}, title = {Unsupervised Learning of Invariances in a Simple Model of the Visual System.}, booktitle = {Proc. 9th Annual Computational Neuroscience Meeting (CNS'00), July 16--20, Brugge, Belgium}, year = {2000}, pages = {157} }
Wiskott, L.	1999	The Role of Topographical Constraints in Face Recognition. [Abstract] [BibTeX] [URL] [URL(2)]	Pattern Recognition Letters , 20(1), 89-96.	article	Topography in face recognition (1995,1997)
Abstract: The role of topographical constraints for recognition performance is investigated systematically for the case of face recognition. Images are represented by rectangular graphs labeled with jets, based on a Gabor wavelet transform. Topographical constraints are varied between rigid and no constraints. A comparison with two elastic graph matching algorithms is made. The simple methods presented in this paper and elastic graph matching perform comparably on easy galleries, i.e. different facial expression or 11° rotation in depth. On a 22° gallery, elastic graph matching performs significantly better.
BibTeX: @article{Wiskott-1999a, author = {Laurenz Wiskott}, title = {The Role of Topographical Constraints in Face Recognition.}, journal = {Pattern Recognition Letters}, year = {1999}, volume = {20}, number = {1}, pages = {89--96}, url = {http://www.sciencedirect.com/science/article/pii/S0167865598001226} }
Wiskott, L.	1999	Learning Invariance Manifolds. [BibTeX]	Proc. Computational Neuroscience Meeting (CNS'98), Santa Barbara, CA, USA .	inproceedings	SFA: Learning visual invariances I (1997-1999)
BibTeX: @inproceedings{Wiskott-1999b, author = {Laurenz Wiskott}, title = {Learning Invariance Manifolds.}, booktitle = {Proc. Computational Neuroscience Meeting (CNS'98), Santa Barbara, CA, USA}, year = {1999} }
Wiskott, L.	1999	Segmentation from Motion: Combining Gabor- and Mallat-Wavelets to Overcome the Aperture and Correspondence Problems. [Abstract] [BibTeX] [URL] [URL(2)]	Pattern Recognition , 32(10), 1751-1766.	article	Segmentation from motion (1993)
Abstract: A new method for segmentation from motion is presented, which is designed to be part of a general object-recognition system. The key idea is to integrate information from Gabor- and Mallat-wavelet transforms of an image sequence to overcome the aperture and the correspondence problem. It is assumed that objects move fronto-parallel. Gabor-wavelet responses allow accurate estimation of image flow vectors with low spatial resolution. A histogram over this image flow field is evaluated and its local maxima provide a set of motion hypotheses. These serve to reduce the correspondence problem occurring in utilizing the Mallat-wavelet transform, which provides the required high spatial resolution in segmentation. Segmentation reliability is improved by integration over time. The system can segment several small, disconnected, and openworked objects, such as dot patterns. Several examples demonstrate the performance of the system and show that the algorithm behaves reasonably well, even if the assumption of fronto-parallel motion is not met.
BibTeX: @article{Wiskott-1999d, author = {Laurenz Wiskott}, title = {Segmentation from Motion: Combining Gabor- and Mallat-Wavelets to Overcome the Aperture and Correspondence Problems.}, journal = {Pattern Recognition}, year = {1999}, volume = {32}, number = {10}, pages = {1751--1766}, url = {http://www.sciencedirect.com/science/article/pii/S0031320398001794} }
Wiskott, L.	1998	Learning Invariance Manifolds. [BibTeX] [URL(2)]	Proc. of the 5th Joint Symp. on Neural Computation, May 16, San Diego, CA, USA , 8, 196-203. Publ. Univ. of California, San Diego, CA.	inproceedings	SFA: Learning visual invariances I (1997-1999)
BibTeX: @inproceedings{Wiskott-1998a, author = {Laurenz Wiskott}, title = {Learning Invariance Manifolds.}, booktitle = {Proc. of the 5th Joint Symp. on Neural Computation, May 16, San Diego, CA, USA}, publisher = {Univ. of California}, year = {1998}, volume = {8}, pages = {196--203} }
Wiskott, L.	1997	Phantom Faces for Face Analysis. [Abstract] [BibTeX] [URL] [URL(2)]	Pattern Recognition , 30(6), 837-846.	article	Face analysis with EBGM (1993-1995)
Abstract: The system presented is part of a general object recognition system. Images of faces are represented as graphs, labeled with topographical information and local features. New graphs of faces are generated by an elastic graph matching procedure comparing the new face with a composition of stored graphs: the face bunch graph. The result of this matching process can be used to generate composite images of faces and to determine facial attributes represented in the bunch graph, such as sex or the presence of glasses or a beard.
BibTeX: @article{Wiskott-1997a, author = {Laurenz Wiskott}, title = {Phantom Faces for Face Analysis.}, journal = {Pattern Recognition}, year = {1997}, volume = {30}, number = {6}, pages = {837--846}, url = {http://www.sciencedirect.com/science/article/pii/S003132039600132X} }
Wiskott, L.	1997	Phantom Faces for Face Analysis. [BibTeX]	Proc. IEEE Intl. Conf. on Image Processing (ICIP'97), Santa Barbara, CA, USA , III 308-311. Publ. IEEE.	inproceedings	Face analysis with EBGM (1993-1995)
BibTeX: @inproceedings{Wiskott-1997d, author = {Laurenz Wiskott}, title = {Phantom Faces for Face Analysis.}, booktitle = {Proc. IEEE Intl. Conf. on Image Processing (ICIP'97), Santa Barbara, CA, USA}, publisher = {IEEE}, year = {1997}, pages = {III 308--311} }
Wiskott, L.	1996	Phantom Faces for Face Analysis. [BibTeX]	Technical report , IR-INI 96-06. Publ. Institut für Neuroinformatik, Ruhr University Bochum, D-44780 Bochum, Germany.	techreport	Face analysis with EBGM (1993-1995)
BibTeX: @techreport{Wiskott-1996a, author = {Laurenz Wiskott}, title = {Phantom Faces for Face Analysis.}, publisher = {Institut für Neuroinformatik}, year = {1996}, volume = {IR-INI 96-06}, howpublished = {Technical report} }
Wiskott, L.	1996	Segmentation from Motion: Combining Gabor- and Mallat-Wavelets to Overcome Aperture and Correspondence Problem. [BibTeX]	Technical report , IR-INI 96-10. Publ. Institut für Neuroinformatik, Ruhr University Bochum, D-44780 Bochum, Germany.	techreport	Segmentation from motion (1993)
BibTeX: @techreport{Wiskott-1996b, author = {Laurenz Wiskott}, title = {Segmentation from Motion: Combining Gabor- and Mallat-Wavelets to Overcome Aperture and Correspondence Problem.}, publisher = {Institut für Neuroinformatik}, year = {1996}, volume = {IR-INI 96-10}, howpublished = {Technical report} }
Wiskott, L.	1996	Phantom Faces for Face Analysis. [BibTeX]	Proc. of the 3rd Joint Symp. on Neural Computation, June 1, Pasadena, CA, USA , 6, 46-52. Publ. Univ. of California, San Diego, CA.	inproceedings	Face analysis with EBGM (1993-1995)
BibTeX: @inproceedings{Wiskott-1996c, author = {Laurenz Wiskott}, title = {Phantom Faces for Face Analysis.}, booktitle = {Proc. of the 3rd Joint Symp. on Neural Computation, June 1, Pasadena, CA, USA}, publisher = {Univ. of California}, year = {1996}, volume = {6}, pages = {46--52} }
Wiskott, L.	1995	Labeled Graphs and Dynamic Link Matching for Face Recognition and Scene Analysis. [BibTeX] [URL(2)]	, Reihe Physik , 53. Publ. Verlag Harri Deutsch, Thun, Frankfurt am Main, Germany.	book	Scene analysis (1992), Face recognition with DLM (1993-1995), Face analysis with EBGM (1993-1995), Face recognition with EBGM (1993,1994)
BibTeX: @book{Wiskott-1995, author = {Laurenz Wiskott}, title = {Labeled Graphs and Dynamic Link Matching for Face Recognition and Scene Analysis.}, publisher = {Verlag Harri Deutsch}, year = {1995}, volume = {53} }
Wiskott, L.; Appleby, P.A. & Kempermann, G.	2007	Adult hippocampal neurogenesis - a strategy for avoiding catastrophic interference? [BibTeX]	Proc. 3rd Annual Computational Cognitive Neuroscience Conference, Nov. 1-2, San Diego, CA, USA , 9. Eds. Becker, S. & others	inproceedings	Adult neurogenesis: Function II (2005-2007)
BibTeX: @inproceedings{WiskottApplebyEtAl-2007b, author = {Laurenz Wiskott and Peter A. Appleby and Gerd Kempermann}, title = {Adult hippocampal neurogenesis - a strategy for avoiding catastrophic interference?}, booktitle = {Proc. 3rd Annual Computational Cognitive Neuroscience Conference, Nov. 1--2, San Diego, CA, USA}, year = {2007}, pages = {9} }
Wiskott, L.; Appleby, P.A. & Kempermann, G.	2007	What is the functional role of adult neurogenesis in the hippocampus? - A computational approach. [BibTeX]	Proc. Adult Neurogenesis Symposium, Oct. 15, Dresden, Germany . Ed. Kempermann, G. Publ. Abcam, Cambridge, UK, EU.	inproceedings	Adult neurogenesis: Function II (2005-2007)
BibTeX: @inproceedings{WiskottApplebyEtAl-2007a, author = {Laurenz Wiskott and Peter A. Appleby and Gerd Kempermann}, title = {What is the functional role of adult neurogenesis in the hippocampus? - A computational approach.}, booktitle = {Proc. Adult Neurogenesis Symposium, Oct. 15, Dresden, Germany}, publisher = {Abcam}, year = {2007} }
Wiskott, L. & Berkes, P.	2002	Is slowness a principle for the emergence of complex cells in primary visual cortex? [BibTeX]	Proc. Berlin Neuroscience Forum, Apr. 18-20, Liebenwalde, Germany , 43. Ed. Kettenmann, H. Publ. Max-Delbrück-Centrum für Molekulare Medizin (MDC), Berlin.	inproceedings	SFA: Complex cells (2001-2003)
BibTeX: @inproceedings{WiskottBerkes-2002, author = {Laurenz Wiskott and Pietro Berkes}, title = {Is slowness a principle for the emergence of complex cells in primary visual cortex?}, booktitle = {Proc. Berlin Neuroscience Forum, Apr. 18-20, Liebenwalde, Germany}, publisher = {Max-Delbrück-Centrum für Molekulare Medizin (MDC)}, year = {2002}, pages = {43} }
Wiskott, L. & Berkes, P.	2003	Is Slowness a Learning Principle of the Visual Cortex? [BibTeX]	Proc. Jahrestagung der Deutschen Zoologischen Gesellschaft, June 9-13, Berlin, Germany .	inproceedings	SFA: Learning visual invariances I (1997-1999), SFA: Theory of free responses (1998-2002), SFA: Complex cells (2001-2003)
BibTeX: @inproceedings{WiskottBerkes-2003, author = {Laurenz Wiskott and Pietro Berkes}, title = {Is Slowness a Learning Principle of the Visual Cortex?}, booktitle = {Proc. Jahrestagung der Deutschen Zoologischen Gesellschaft, June 9-13, Berlin, Germany}, year = {2003} }
Wiskott, L.; Berkes, P.; Franzius, M.; Sprekeler, H. & Wilbert, N.	2011	Slow feature analysis. [Abstract] [BibTeX] [URL]	Scholarpedia , 6(4), 5282.	article	SFA: Theory of free responses (1998-2002), SFA: Estimating driving forces (2000-2003), SFA: Complex cells (2001-2003), SFA: Place cells (2003-2007), SFA: Theory of complex cells (2004-2007), Extended slow feature analysis (xSFA) (2006-2008), SFA: Learning visual invariances II (2006-2009)
Abstract: Slow feature analysis (SFA) is an unsupervised learning algorithm for extracting slowly varying features from a quickly varying input signal. It has been successfully applied, e.g., to the self-organization of complex-cell receptive fields, the recognition of whole objects invariant to spatial transformations, the self-organization of place-cells, extraction of driving forces, and to nonlinear blind source separation.
BibTeX: @article{WiskottBerkesEtAl-2011, author = {Wiskott, L. and Berkes, P. and Franzius, M. and Sprekeler, H. and Wilbert, N.}, title = {Slow feature analysis.}, journal = {Scholarpedia}, year = {2011}, volume = {6}, number = {4}, pages = {5282}, url = {http://www.scholarpedia.org/article/Slow_feature_analysis} }
Wiskott, L.; Fellous, J.-M.; Krüger, N. & von der Malsburg, C.	1995	Face Recognition and Gender Determination. [BibTeX] [URL(2)]	Proc. Intl. Workshop on Automatic Face- and Gesture-Recognition (IWAFGR'95), Zurich, Switzerland , 92-97. Ed. Bichsel, M. Publ. MultiMedia Laboratory, University of Zurich.	inproceedings	Face recognition with EBGM (1993,1994), Face analysis with EBGM (1993-1995)
BibTeX: @inproceedings{WiskottFellousEtAl-1995, author = {Laurenz Wiskott and Jean-Marc Fellous and Norbert Krüger and Christoph von der Malsburg}, title = {Face Recognition and Gender Determination.}, booktitle = {Proc. Intl. Workshop on Automatic Face- and Gesture-Recognition (IWAFGR'95), Zurich, Switzerland}, publisher = {MultiMedia Laboratory, University of Zurich}, year = {1995}, pages = {92--97} }
Wiskott, L.; Fellous, J.-M.; Krüger, N. & von der Malsburg, C.	1999	Face Recognition by Elastic Bunch Graph Matching. [Abstract] [BibTeX] [URL(2)]	Chapter 11 in Intelligent Biometric Techniques in Fingerprint and Face Recognition , 355-396. Eds. Jain, L. C.; Halici, U.; Hayashi, I. & Lee, S. B. Publ. CRC Press.	incollection	Face recognition with EBGM (1993,1994)
Abstract: We present a system for recognizing human faces from single images out of a large database containing one image per person. The task is difficult because of image variation in terms of position, size, expression, and pose. The system collapses most of this variance by extracting concise face descriptions in the form of image graphs. In these, fiducial points on the face (eyes, mouth, etc.) are described by sets of wavelet components (jets). Image graph extraction is based on a novel approach, the bunch graph, which is constructed from a small set of sample image graphs. Recognition is based on a straightforward comparison of image graphs. We report recognition experiments on the FERET database as well as the Bochum database, including recognition across pose.
BibTeX: @incollection{WiskottFellousEtAl-1999, author = {Laurenz Wiskott and Jean-Marc Fellous and Norbert Krüger and Christoph von der Malsburg}, title = {Face Recognition by Elastic Bunch Graph Matching.}, booktitle = {Intelligent Biometric Techniques in Fingerprint and Face Recognition}, publisher = {CRC Press}, year = {1999}, pages = {355--396} }
Wiskott, L.; Fellous, J.-M.; Krüger, N. & von der Malsburg, C.	1997	Face Recognition by Elastic Bunch Graph Matching. [BibTeX]	Proc. 7th Intl. Conf. on Computer Analysis of Images and Patterns (CAIP'97), Kiel, Germany , Lecture Notes in Computer Science , 1296, 456-463. Eds. Sommer, G.; Daniilidis, K. & Pauli, J. Publ. Springer-Verlag, Heidelberg.	inproceedings	Face recognition with EBGM (1993,1994)
BibTeX: @inproceedings{WiskottFellousEtAl-1997b, author = {Laurenz Wiskott and Jean-Marc Fellous and Norbert Krüger and Christoph von der Malsburg}, title = {Face Recognition by Elastic Bunch Graph Matching.}, booktitle = {Proc. 7th Intl. Conf. on Computer Analysis of Images and Patterns (CAIP'97), Kiel, Germany}, publisher = {Springer-Verlag}, year = {1997}, volume = {1296}, pages = {456--463} }
Wiskott, L.; Fellous, J.-M.; Krüger, N. & von der Malsburg, C.	1997	Face Recognition by Elastic Bunch Graph Matching. [Abstract] [BibTeX] [URL] [URL(2)]	IEEE Trans. on Pattern Analysis and Machine Intelligence , 19(7), 775-779.	article	Face recognition with EBGM (1993,1994)
Abstract: We present a system for recognizing human faces from single images out of a large database containing one image per person. Faces are represented by labeled graphs, based on a Gabor wavelet transform. Image graphs of new faces are extracted by an elastic graph matching process and can be compared by a simple similarity function. The system differs from the preceding one [LadVorBuh93] in three respects. Phase information is used for accurate node positioning. Object-adapted graphs are used to handle large rotations in depth. Image graph extraction is based on a novel data structure, the bunch graph, which is constructed from a small set of sample image graphs.
BibTeX: @article{WiskottFellousEtAl-1997a, author = {Laurenz Wiskott and Jean-Marc Fellous and Norbert Krüger and Christoph von der Malsburg}, title = {Face Recognition by Elastic Bunch Graph Matching.}, journal = {IEEE Trans. on Pattern Analysis and Machine Intelligence}, year = {1997}, volume = {19}, number = {7}, pages = {775--779}, url = {http://doi.ieeecomputersociety.org/10.1109/34.598235} }
Wiskott, L.; Fellous, J.-M.; Krüger, N. & von der Malsburg, C.	1997	Face Recognition by Elastic Bunch Graph Matching. [BibTeX]	Proc. IEEE Intl. Conf. on Image Processing (ICIP'97), Santa Barbara, CA, USA , I 129-132. Publ. IEEE.	inproceedings	Face recognition with EBGM (1993,1994)
BibTeX: @inproceedings{WiskottFellousEtAl-1997c, author = {Laurenz Wiskott and Jean-Marc Fellous and Norbert Krüger and Christoph von der Malsburg}, title = {Face Recognition by Elastic Bunch Graph Matching.}, booktitle = {Proc. IEEE Intl. Conf. on Image Processing (ICIP'97), Santa Barbara, CA, USA}, publisher = {IEEE}, year = {1997}, pages = {I 129--132} }
Wiskott, L.; Fellous, J.-M.; Krüger, N. & von der Malsburg, C.	1996	Face Recognition by Elastic Bunch Graph Matching. [BibTeX]	Technical report , IR-INI 96-08. Publ. Institut für Neuroinformatik, Ruhr University Bochum, D-44780 Bochum, Germany.	techreport	Face recognition with EBGM (1993,1994)
BibTeX: @techreport{WiskottFellousEtAl-1996, author = {Laurenz Wiskott and Jean-Marc Fellous and Norbert Krüger and Christoph von der Malsburg}, title = {Face Recognition by Elastic Bunch Graph Matching.}, publisher = {Institut für Neuroinformatik}, year = {1996}, volume = {IR-INI 96-08}, howpublished = {Technical report} }
Wiskott, L.; Franzius, M.; Berkes, P. & Sprekeler, H.	2007	Is slowness a learning principle of the visual system? [BibTeX]	Proc. 39th European Brain and Behaviour Society Meeting (EBBS), Sep. 15-19, Triest, Italy , 14-15. Eds. Treves, A.; Battaglini, P. P.; Chelazzi, L.; Diamond, M. & Vallortigara, G.	inproceedings	SFA: Complex cells (2001-2003), SFA: Theory of complex cells (2004-2007), SFA: Learning visual invariances II (2006-2009)
BibTeX: @inproceedings{WiskottFranziusEtAl-2007, author = {Laurenz Wiskott and Mathias Franzius and Pietro Berkes and Henning Sprekeler}, title = {Is slowness a learning principle of the visual system?}, booktitle = {Proc. 39th European Brain and Behaviour Society Meeting (EBBS), Sep. 15--19, Triest, Italy}, year = {2007}, pages = {14--15} }
Wiskott, L.; Franzius, M.; Sprekeler, H. & Appleby, P.	2009	Self-organization of place cells with slowness, sparseness, and neurogenesis. [BibTeX]	Proc. 41st European Brain and Behaviour Society Meeting (EBBS), Sep. 13-18, Rhodes Island, Greece .	inproceedings	SFA: Place cells (2003-2007), Adult neurogenesis: Function III (2007-2010)
BibTeX: @inproceedings{WiskottFranziusEtAl-2009, author = {Laurenz Wiskott and Mathias Franzius and Henning Sprekeler and Peter Appleby}, title = {Self-organization of place cells with slowness, sparseness, and neurogenesis.}, booktitle = {Proc. 41st European Brain and Behaviour Society Meeting (EBBS), Sep. 13--18, Rhodes Island, Greece}, year = {2009} }
Wiskott, L. & von der Malsburg, C.	1995	Face Recognition by Dynamic Link Matching. [BibTeX]	Proc. Intl. Conf. on Artificial Neural Networks (ICANN'95), Paris, France , 347-352. Eds. F. Fogelman-Soulié, J. C. Rault, P. G. & Dreyfus, G. Publ. EC2 & Cie, Paris.	inproceedings	Face recognition with DLM (1993-1995)
BibTeX: @inproceedings{WiskottMalsburg-1995, author = {Laurenz Wiskott and Christoph von der Malsburg}, title = {Face Recognition by Dynamic Link Matching.}, booktitle = {Proc. Intl. Conf. on Artificial Neural Networks (ICANN'95), Paris, France}, publisher = {EC2 & Cie}, year = {1995}, pages = {347--352} }
Wiskott, L. & von der Malsburg, C.	1994	A Neural System for the Recognition of Partially Occluded Objects in Cluttered Scenes. [BibTeX]	Advances in Pattern Recognition Systems using Neural Networks Technologies , Machine Perception and Artificial Intelligence , 7. Eds. Guyon, I. & Wang, P. S. P. Publ. World scientific.	incollection	Scene analysis (1992)
BibTeX: @incollection{WiskottMalsburg-1994a, author = {Laurenz Wiskott and Christoph von der Malsburg}, title = {A Neural System for the Recognition of Partially Occluded Objects in Cluttered Scenes.}, booktitle = {Advances in Pattern Recognition Systems using Neural Networks Technologies}, publisher = {World scientific}, year = {1994}, volume = {7} }
Wiskott, L. & von der Malsburg, C.	1994	Object Recognition with Dynamic Link Matching. [BibTeX]	Neural Computing , Dagstuhl-Seminar-Report , 103, 20-21. Eds. Maass, W.; von der Malsburg, C.; Sontag, E. & Wegener, I. Publ. Schloss Dagstuhl, D-66687 Wadern, Germany.	inproceedings	Face recognition with DLM (1993-1995)
BibTeX: @inproceedings{WiskottMalsburg-1994b, author = {Laurenz Wiskott and Christoph von der Malsburg}, title = {Object Recognition with Dynamic Link Matching.}, booktitle = {Neural Computing}, publisher = {Schloss Dagstuhl}, year = {1994}, volume = {103}, pages = {20--21} }
Wiskott, L. & von der Malsburg, C.	1999	Objekterkennung in einem selbstorganisierenden neuronalen System. [BibTeX]	Komplexe Systeme und Nichtlineare Dynamik in Natur und Gesellschaft , 169-188. Ed. Mainzer, K. Publ. Springer-Verlag.	incollection	Face recognition with DLM (1993-1995)
BibTeX: @incollection{WiskottMalsburg-1999, author = {Laurenz Wiskott and Christoph von der Malsburg}, title = {Objekterkennung in einem selbstorganisierenden neuronalen System.}, booktitle = {Komplexe Systeme und Nichtlineare Dynamik in Natur und Gesellschaft}, publisher = {Springer-Verlag}, year = {1999}, pages = {169--188} }
Wiskott, L. & von der Malsburg, C.	1996	Face Recognition by Dynamic Link Matching. [Abstract] [BibTeX] [URL] [URL(2)]	Chapter 11 in Lateral Interactions in the Cortex: Structure and Function . Eds. Sirosh, J.; Miikkulainen, R. & Choe, Y. Publ. The UTCS Neural Networks Research Group, Austin, TX.	incollection	Face recognition with DLM (1993-1995)
Abstract: We present a neural system for the recognition of objects from realistic images, together with results of tests of face recognition from a large gallery. The system is inherently invariant with respect to shift, and is robust against many other variations, most notably rotation in depth and deformation. The system is based on Dynamic Link Matching. It consists of an image domain and a model domain, which we tentatively identify with primary visual cortex and infero-temporal cortex. Both domains have the form of neural sheets of hypercolumns, which are composed of simple feature detectors (modeled as Gabor-based wavelets). Each object is represented in memory by a separate model sheet, that is, a two-dimensional array of features. The match of the image to the models is performed by network self-organization, in which rapid reversible synaptic plasticity of the connections ("dynamic links") between the two domains is controlled by signal correlations, which are shaped by fixed inter-columnar connections and by the dynamic links themselves. The system requires very little genetic or learned structure, relying essentially on the rules of rapid synaptic plasticity and the a priori constraint of preservation of topography to find matches. This constraint is encoded within the neural sheets with the help of lateral connections, which are excitatory over short range and inhibitory over long range.
BibTeX: @incollection{WiskottMalsburg-1996d, author = {Laurenz Wiskott and Christoph von der Malsburg}, title = {Face Recognition by Dynamic Link Matching.}, booktitle = {Lateral Interactions in the Cortex: Structure and Function}, publisher = {The UTCS Neural Networks Research Group, Austin, TX}, year = {1996}, url = {http://www.cs.utexas.edu/users/nn/web-pubs/htmlbook96/} }
Wiskott, L. & von der Malsburg, C.	1996	Recognizing Faces by Dynamic Link Matching. [BibTeX]	Symposium über biologische Informationsverarbeitung und Neuronale Netze (SINN'95), Germany , Beiträge zur wissenschaftlichen Diskussion , 16, 63-68. Eds. Wismüller, A. & Dersch, D. R. Publ. Hanns-Seidel-Stiftung, Munich, Germany.	inproceedings	Face recognition with DLM (1993-1995)
BibTeX: @inproceedings{WiskottMalsburg-1996c, author = {Laurenz Wiskott and Christoph von der Malsburg}, title = {Recognizing Faces by Dynamic Link Matching.}, booktitle = {Symposium über biologische Informationsverarbeitung und Neuronale Netze (SINN'95), Germany}, publisher = {Hanns-Seidel-Stiftung}, year = {1996}, volume = {16}, pages = {63--68} }
Wiskott, L. & von der Malsburg, C.	2003	Labeled Bunch Graphs for Image Analysis. [BibTeX]	United States Patent , 6,563,950.	misc	Face recognition with EBGM (1993,1994)
BibTeX: @misc{WiskottMalsburg-2003, author = {Laurenz Wiskott and Christoph von der Malsburg}, title = {Labeled Bunch Graphs for Image Analysis.}, year = {2003}, volume = {6,563,950}, howpublished = {United States Patent} }
Wiskott, L. & von der Malsburg, C.	2002	Labeled Bunch Graphs for Image Analysis. [BibTeX]	United States Patent , 6,356,659.	misc	Face recognition with EBGM (1993,1994)
BibTeX: @misc{WiskottMalsburg-2002, author = {Laurenz Wiskott and Christoph von der Malsburg}, title = {Labeled Bunch Graphs for Image Analysis.}, year = {2002}, volume = {6,356,659}, howpublished = {United States Patent} }
Wiskott, L. & von der Malsburg, C.	2001	Labeled Bunch Graphs for Image Analysis. [BibTeX]	United States Patent , 6,222,939.	misc	Face recognition with EBGM (1993,1994)
BibTeX: @misc{WiskottMalsburg-2001, author = {Laurenz Wiskott and Christoph von der Malsburg}, title = {Labeled Bunch Graphs for Image Analysis.}, year = {2001}, volume = {6,222,939}, howpublished = {United States Patent} }
Wiskott, L. & von der Malsburg, C.	1996	Face Recognition by Dynamic Link Matching. [BibTeX] [URL(2)]	Technical report , IR-INI 96-05. Publ. Institut für Neuroinformatik, Ruhr University Bochum, D-44780 Bochum, Germany.	techreport	Face recognition with DLM (1993-1995)
BibTeX: @techreport{WiskottMalsburg-1996a, author = {Laurenz Wiskott and Christoph von der Malsburg}, title = {Face Recognition by Dynamic Link Matching.}, publisher = {Institut für Neuroinformatik}, year = {1996}, volume = {IR-INI 96-05}, howpublished = {Technical report} }
Wiskott, L. & von der Malsburg, C.	1996	Recognizing Faces by Dynamic Link Matching. [BibTeX]	Proc. US-EC Workshop on Neuroinformatics 1995, Washington DC, USA .	inproceedings	Face recognition with DLM (1993-1995)
BibTeX: @inproceedings{WiskottMalsburg-1996b, author = {Laurenz Wiskott and Christoph von der Malsburg}, title = {Recognizing Faces by Dynamic Link Matching.}, booktitle = {Proc. US-EC Workshop on Neuroinformatics 1995, Washington DC, USA}, year = {1996} }
Wiskott, L. & von der Malsburg, C.	1993	A Neural System for the Recognition of Partially Occluded Objects in Cluttered Scenes. [Abstract] [BibTeX] [URL] [URL(2)]	Intl. J. of Pattern Recognition and Artificial Intelligence , 7(4), 935-948.	article	Scene analysis (1992)
Abstract: We present a system for the interpretation of camera images of scenes composed of several known objects with mutual occlusion. The scenes are analyzed by the recognition of the objects present and by the determination of their occlusion relations. Objects are internally represented by stored model graphs. These are formed in a semi-automatic way by showing objects against a varying background. Objects are recognized by dynamic link matching. Our experiments show that our system is very successful in analyzing cluttered scenes. The system architecture goes beyond classical neural networks by making extensive use of flexible links between units, as proposed in the dynamic link architecture. The present implementation is, however, rather algorithmic in style and is to be regarded as a pilot study that is preparing the way for a detailed implementation of the architecture.
BibTeX: @article{WiskottMalsburg-1993, author = {Laurenz Wiskott and Christoph von der Malsburg}, title = {A Neural System for the Recognition of Partially Occluded Objects in Cluttered Scenes.}, journal = {Intl. J. of Pattern Recognition and Artificial Intelligence}, year = {1993}, volume = {7}, number = {4}, pages = {935--948}, url = {http://dx.doi.org/10.1142/S0218001493000479} }
Wiskott, L.; von der Malsburg, C. & Weitzenfeld, A.	2002	Face Recognition by Dynamic Link Matching. [BibTeX]	Chapter 18 in The Neural Simulation Language: A System for Brain Modeling , 343-372. Eds. Weitzenfeld, A.; Arbib, M. A. & Alexander, A. Publ. MIT Press, Cambridge, MA, USA.	incollection	Face recognition with DLM (1993-1995)
BibTeX: @incollection{WiskottMalsburgEtAl-2002, author = {Laurenz Wiskott and Christoph von der Malsburg and Alfredo Weitzenfeld}, title = {Face Recognition by Dynamic Link Matching.}, booktitle = {The Neural Simulation Language: A System for Brain Modeling}, publisher = {MIT Press}, year = {2002}, pages = {343--372} }
Wiskott, L.; Quang, M.H.; Sprekeler, H. & Zito, T.	2010	Slow Feature Analysis: Analyzing Signals with the Slowness Principle. [BibTeX]	Proc. 2nd joint Statistical Meeting Deutsche Arbeitsgemeinschaft Statistik (DAGStat'10), Mar. 23-26, Dortmund, Germany , 398. Publ. Technische Universität Dortmund.	inproceedings	SFA: Estimating driving forces (2000-2003), Extended slow feature analysis (xSFA) (2006-2008)
BibTeX: @inproceedings{WiskottQuangEtAl-2010, author = {Laurenz Wiskott and Minh Ha Quang and Henning Sprekeler and Tiziano Zito}, title = {Slow Feature Analysis: Analyzing Signals with the Slowness Principle.}, booktitle = {Proc. 2nd joint Statistical Meeting Deutsche Arbeitsgemeinschaft Statistik (DAGStat'10), Mar. 23--26, Dortmund, Germany}, publisher = {Technische Universität Dortmund}, year = {2010}, pages = {398} }
Wiskott, L.; Rasch, M. & Kempermann, G.	2006	A functional hypothesis for adult hippocampal neurogenesis: Avoidance of catastrophic interference in the dentate gyrus. [Abstract] [BibTeX] [URL] [URL(2)]	Hippocampus , 16(3), 329-343.	article	Adult neurogenesis: Function I (2000-2003)
Abstract: The dentate gyrus is part of the hippocampal memory system and special in that it generates new neurons throughout life. Here we discuss the question of what the functional role of these new neurons might be. Our hypothesis is that they help the dentate gyrus to avoid the problem of catastrophic interference when adapting to new environments. We assume that old neurons are rather stable and preserve an optimal encoding learned for known environments while new neurons are plastic to adapt to those features that are qualitatively new in a new environment. A simple network simulation demonstrates that adding new plastic neurons is indeed a successful strategy for adaptation without catastrophic interference.
BibTeX: @article{WiskottRaschEtAl-2006, author = {Laurenz Wiskott and Malte Rasch and Gerd Kempermann}, title = {A functional hypothesis for adult hippocampal neurogenesis: Avoidance of catastrophic interference in the dentate gyrus.}, journal = {Hippocampus}, year = {2006}, volume = {16}, number = {3}, pages = {329--343}, url = {http://onlinelibrary.wiley.com/doi/10.1002/hipo.20167/abstract} }
Wiskott, L.; Rasch, M. & Kempermann, G.	2005	What is the functional role of adult neurogenesis in the hippocampus? [BibTeX]	Proc. Computational and Systems Neuroscience (COSYNE'05), Salk Lake City, USA .	inproceedings	Adult neurogenesis: Function I (2000-2003)
BibTeX: @inproceedings{WiskottRaschEtAl-2005, author = {Laurenz Wiskott and Malte Rasch and Gerd Kempermann}, title = {What is the functional role of adult neurogenesis in the hippocampus?}, booktitle = {Proc. Computational and Systems Neuroscience (COSYNE'05), Salk Lake City, USA}, year = {2005} }
Wiskott, L.; Rasch, M. & Kempermann, G.	2004	What is the functional role of adult neurogenesis in the hippocampus? [Abstract] [BibTeX] [URL] [URL(2)]	Cognitive Sciences EPrint Archive (CogPrints) , 4012.	misc	Adult neurogenesis: Function I (2000-2003)
Abstract: The dentate gyrus is part of the hippocampal memory system and special in that it generates new neurons throughout life. Here we discuss the question of what the functional role of these new neurons might be. Our hypothesis is that they help the dentate gyrus to avoid the problem of catastrophic interference when adapting to new environments. We assume that old neurons are rather stable and preserve an optimal encoding learned for known environments while new neurons are plastic to adapt to those features that are qualitatively new in a new environment. A simple network simulation demonstrates that adding new plastic neurons is indeed a successful strategy for adaptation without catastrophic interference.
BibTeX: @misc{WiskottRaschEtAl-2004, author = {Laurenz Wiskott and Malte Rasch and Gerd Kempermann}, title = {What is the functional role of adult neurogenesis in the hippocampus?}, year = {2004}, volume = {4012}, howpublished = {Cognitive Sciences EPrint Archive (CogPrints)}, url = {http://cogprints.org/4012/} }
Wiskott, L.; Rasch, M.J. & Kempermann, G.	2007	What is the functional role of adult neurogenesis in the hippocampus? [BibTeX]	Kognitionsforschung 2007, Beiträge zur 8. Jahrestagung der Gesellschaft für Kognitionswissenschaft (KogWis'07), Mar. 19-21, Saarbrücken, Germany , 53. Eds. Frings, C.; Mecklinger, A.; Opitz, B.; Pospeschill, M.; Wentura, D. & Zimmer, H. D. Publ. Shaker Verlag, Aachen.	inproceedings	Adult neurogenesis: Function I (2000-2003)
BibTeX: @inproceedings{WiskottRaschEtAl-2007, author = {Laurenz Wiskott and Malte J. Rasch and Gerd Kempermann}, title = {What is the functional role of adult neurogenesis in the hippocampus?}, booktitle = {Kognitionsforschung 2007, Beiträge zur 8. Jahrestagung der Gesellschaft für Kognitionswissenschaft (KogWis'07), Mar. 19--21, Saarbrücken, Germany}, publisher = {Shaker Verlag}, year = {2007}, pages = {53} }
Wiskott, L. & Sejnowski, T.	1997	Objective Functions for Neural Map Formation. [BibTeX]	Proc. 7th Intl. Conf. on Artificial Neural Networks (ICANN'97), Lausanne, Switzerland , Lecture Notes in Computer Science , 1327, 243-248. Eds. Gerstner, W.; Germond, A.; Hasler, M. & Nicoud, J.-D. Publ. Springer-Verlag, Berlin, Germany.	inproceedings	Neural map formation (1996,1997)
BibTeX: @inproceedings{WiskottSejnowski-1997c, author = {Laurenz Wiskott and Terrence Sejnowski}, title = {Objective Functions for Neural Map Formation.}, booktitle = {Proc. 7th Intl. Conf. on Artificial Neural Networks (ICANN'97), Lausanne, Switzerland}, publisher = {Springer-Verlag}, year = {1997}, volume = {1327}, pages = {243--248} }
Wiskott, L. & Sejnowski, T.	2001	Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization. [BibTeX]	Self-organizing map formation: Foundations of neural computation. , 83-128. Eds. Obermayer, K. & Sejnowski, T. J. Publ. MIT Press, Cambridge, MA, USA.	incollection	Neural map formation (1996,1997)
BibTeX: @incollection{WiskottSejnowski-2001, author = {Laurenz Wiskott and Terrence Sejnowski}, title = {Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization.}, booktitle = {Self-organizing map formation: Foundations of neural computation.}, publisher = {MIT Press}, year = {2001}, pages = {83--128} }
Wiskott, L. & Sejnowski, T.	2002	Slow Feature Analysis: Unsupervised Learning of Invariances. [Abstract] [BibTeX] [URL] [URL(2)]	Neural Computation , 14(4), 715-770.	article	SFA: Learning visual invariances I (1997-1999)
Abstract: Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. SFA is based on a non-linear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high dimensional input signals and to extract complex features. Slow feature analysis is applied first to complex cell tuning properties based on simple cell output including disparity and motion. Then, more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA-modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending only on the training stimulus. Surprisingly, only a few training objects sufficed to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades, if the network is trained to learn multiple invariances simultaneously.
BibTeX: @article{WiskottSejnowski-2002, author = {Laurenz Wiskott and Terrence Sejnowski}, title = {Slow Feature Analysis: Unsupervised Learning of Invariances.}, journal = {Neural Computation}, year = {2002}, volume = {14}, number = {4}, pages = {715--770}, url = {http://dx.doi.org/10.1162/089976602317318938}, doi = {http://dx.doi.org/10.1162/089976602317318938} }
Wiskott, L. & Sejnowski, T.	1998	Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization. [Abstract] [BibTeX] [URL] [URL(2)]	Neural Computation , 10(3), 671-716.	article	Neural map formation (1996,1997)
Abstract: Computational models of neural map formation can be considered on at least three different levels of abstraction: detailed models including neural activity dynamics, weight dynamics that abstract from the neural activity dynamics by an adiabatic approximation, and constrained optimization from which equations governing weight dynamics can be derived. Constrained optimization uses an objective function, from which a weight growth rule can be derived as a gradient flow, and some constraints, from which normalization rules are derived. In this paper we present an example of how an optimization problem can be derived from detailed non-linear neural dynamics. A systematic investigation reveals how different weight dynamics introduced previously can be derived from two types of objective function terms and two types of constraints. This includes dynamic link matching as a special case of neural map formation. We focus in particular on the role of coordinate transformations to derive different weight dynamics from the same optimization problem. Several examples illustrate how the constrained optimization framework can help in understanding, generating, and comparing different models of neural map formation. The techniques used in this analysis may also be useful in investigating other types of neural dynamics.
BibTeX: @article{WiskottSejnowski-1998, author = {Laurenz Wiskott and Terrence Sejnowski}, title = {Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization.}, journal = {Neural Computation}, year = {1998}, volume = {10}, number = {3}, pages = {671--716}, url = {http://www.mitpressjournals.org/doi/abs/10.1162/089976698300017700} }
Wiskott, L. & Sejnowski, T.	1997	Objective Functions for Neural Map Formation. [BibTeX]	Technical report , INC-9701. Publ. Institute for Neural Computation, University of California, San Diego, La Jolla, CA 92093, USA.	techreport	Neural map formation (1996,1997)
BibTeX: @techreport{WiskottSejnowski-1997a, author = {Laurenz Wiskott and Terrence Sejnowski}, title = {Objective Functions for Neural Map Formation.}, publisher = {Institute for Neural Computation}, year = {1997}, volume = {INC-9701}, howpublished = {Technical report} }
Wiskott, L. & Sejnowski, T.	1997	Objective Functions for Neural Map Formation. [BibTeX]	Proc. of the 4th Joint Symp. on Neural Computation, May 17, Los Angeles, CA, USA , 7, 242-248. Publ. Univ. of California, San Diego, CA.	inproceedings	Neural map formation (1996,1997)
BibTeX: @inproceedings{WiskottSejnowski-1997b, author = {Laurenz Wiskott and Terrence Sejnowski}, title = {Objective Functions for Neural Map Formation.}, booktitle = {Proc. of the 4th Joint Symp. on Neural Computation, May 17, Los Angeles, CA, USA}, publisher = {Univ. of California}, year = {1997}, volume = {7}, pages = {242--248} }
Wiskott, L.; Sprekeler, H. & Berkes, P.	2007	Towards an analytical derivation of complex cell receptive field properties. [BibTeX]	Proc. 7th Göttingen Meeting of the German Neuroscience Society, Mar. 29 - Apr. 1, Göttingen, Germany , S12-2.	inproceedings	SFA: Complex cells (2001-2003), SFA: Theory of complex cells (2004-2007)
BibTeX: @inproceedings{WiskottSprekelerEtAl-2007, author = {Laurenz Wiskott and Henning Sprekeler and Pietro Berkes}, title = {Towards an analytical derivation of complex cell receptive field properties.}, booktitle = {Proc. 7th Göttingen Meeting of the German Neuroscience Society, Mar. 29 -- Apr. 1, Göttingen, Germany}, year = {2007}, pages = {S12--2} }
Zito, T.	2012	Exploring the slowness principle in the auditory domain. [Abstract] [BibTeX] [URL]	Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I .	phdthesis	Independent slow feature analysis (ISFA) (2003-2005), MDP: Modular toolkit for data processing (2003-now), Extended slow feature analysis (xSFA) (2006-2008)
Abstract: In this thesis we develop models and algorithms based on the slowness principle in the auditory domain. Several experimental results as well as the successful results in the visual domain indicate that, despite the different nature of the sensory signals, the slowness principle may play an important role in the auditory domain as well, if not in the cortex as a whole. Different modeling approaches have been used, which make use of several alternative representations of the auditory stimuli. We show the limitations of these approaches. In the domain of signal processing, the slowness principle and its straightforward implementation, the Slow Feature Analysis algorithm, has been proven to be useful beyond biologically inspired modeling. A novel algorithm for nonlinear blind source separation is described that is based on a combination of the slowness and the statistical independence principles, and is evaluated on artificial and real-world audio signals. The Modular toolkit for Data Processing open source software library is additionally presented.
BibTeX: @phdthesis{Zito-2012, author = {Tiziano Zito}, title = {Exploring the slowness principle in the auditory domain.}, school = {Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I}, year = {2012}, url = {http://edoc.hu-berlin.de/docviews/abstract.php?id=39096} }
Zito, T.; Wilbert, N.; Wiskott, L. & Berkes, P.	2009	Modular toolkit for Data Processing (MDP): a Python data processing framework. [Abstract] [BibTeX] [URL] [URL(2)]	Frontiers in Neuroinformatics , 2(8). Publ. Frontiers Research Foundation.	article	MDP: Modular toolkit for data processing (2003-now)
Abstract: Modular toolkit for Data Processing (MDP) is a data processing framework written in Python. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. Computations are performed efficiently in terms of speed and memory requirements. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. MDP has been written in the context of theoretical research in neuroscience, but it has been designed to be helpful in any context where trainable data processing algorithms are used. Its simplicity on the user's side, the variety of readily available algorithms, and the reusability of the implemented units make it also a useful educational tool.
BibTeX: @article{ZitoWilbertEtAl-2009, author = {T. Zito and N. Wilbert and L. Wiskott and P. Berkes}, title = {Modular toolkit for Data Processing (MDP): a Python data processing framework.}, journal = {Frontiers in Neuroinformatics}, publisher = {Frontiers Research Foundation}, year = {2009}, volume = {2}, number = {8}, url = {http://www.frontiersin.org/neuroinformatics/paper/10.3389/neuro.11/008.2008/}, doi = {http://dx.doi.org/10.3389/neuro.11.008.2008} }
Zito, T. & Wiskott, L.	2006	Diagonalization of time-delayed covariance matrices does not guarantee statistical independence in high-dimensional feature space. [BibTeX]	Proc. ICA Research Network International Workshop, Sep. 18-19, Liverpool, UK , 120-122.	inproceedings	Independent slow feature analysis (ISFA) (2003-2005)
BibTeX: @inproceedings{ZitoWiskott-2006, author = {Tiziano Zito and Laurenz Wiskott}, title = {Diagonalization of time-delayed covariance matrices does not guarantee statistical independence in high-dimensional feature space.}, booktitle = {Proc. ICA Research Network International Workshop, Sep. 18-19, Liverpool, UK}, year = {2006}, pages = {120--122} }

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List of Publications of the Research Group of Prof. Dr. Laurenz Wiskott

List of Publications of the Research Group of
Prof. Dr. Laurenz Wiskott