NW: Learning Invariances


Invarianzen spielen bei der Wahrnehmung unserer Umgebung eine große Rolle, ohne dass deren Mechanismen eindeutig geklärt sind. Ziel der Arbeiten ist es, das Lernen von Invarianzen im visuellen System zu verstehen, indem biologisch plausible Modelle basierend auf dem Lernprinzip der zeitlichen Langsamkeit entwickelt werden. Mittels Videosequenzen soll gezeigt werden, dass ein invariantes Antwortverhalten, wie es bei Neuronen in den höheren Arealen des visuellen Systems gefunden wurde, unter realistischen Bedingungen gelernt und das Lernprinzip in einem lokalen Verbund spikender Modellneuronen in biologisch plausibler Weise realisiert werden kann. In einem hierarchischen Netzwerkmodell soll dann dieses Lernprinzip der zeitlichen Langsamkeit mit anderen Aspekten visueller Wahrnehmung verbunden und gezeigt werden, dass der Ansatz auch für ein integriertes Modell des visuellen Systems geeignet ist.


Principal investigators
Wiskott, Laurenz Prof. Dr. (Details) (Junior Research Groups (Biology))

Financer
Volkswagen-Stiftung (VW)

Duration of project
Start date: 11/2000
End date: 03/2007

Publications

2006



Berkes, P. and Wiskott, L. (2006). On the analysis and interpretation of
inhomogeneous quadratic forms as receptive fields. Neural Computation
(accepted).



Wiskott, L. (2006). How Does Our Visual System Achieve Shift and Size
Invariance? In 23 Problems in Systems Neuroscience, eds. J. L. van Hemmen
and T. J. Sejnowski, publ. Oxford University Press, New York, ISBN-13
978-0-19-514822-0, Chapter 16, pp. 322-340 (review).



Wiskott, L. (1. February 2006). Is Slowness a Learning Principle of Visual
Cortex? Proc. Japan-Germany Symposium on Computational Neuroscience, Wako,
Saitama, Japan, February 1-4, publ. RIKEN Brain Science Institute, p. 25,
(abstract).



2005



Berkes, P. and Wiskott, L. (2005). Analysis of inhomogeneous quadratic
forms for physiological and theoretical studies. Proc. Computational and
Systems Neuroscience, COSYNE'05, Salk Lake City, Utah, March 17-20,
(abstract).



Berkes, P. and Wiskott, L. (2005). On the analysis and interpretation of
inhomogeneous quadratic forms as receptive fields. Cognitive Sciences
EPrint Archive (CogPrints) 4081, http://cogprints.org/4081/.



Berkes, P. and Wiskott, L. (20. July 2005). Slow feature analysis yields a
rich repertoire of complex cell properties. Journal of Vision,
5(6):579-602, http://journalofvision.org/5/6/9/, doi:10.1167/5.6.9.



Blaschke, T. and Wiskott, L. (2005). Nonlinear Blind Source Separation by
Integrating Independent Component Analysis and Slow Feature Analysis.
Proc. Advances in Neural Information Processing Systems 17 (NIPS'04),
Vancouver, December 13-16, 2004.



Wiskott, L., Rasch, M., and Kempermann, G. (2005). What is the functional
role of adult neurogenesis in the hippocampus? Proc. Computational and
Systems Neuroscience, COSYNE'05, Salk Lake City, Utah, March 17-20,
(abstract).



2004



Berkes, P. and Wiskott, L. (2004). Slow feature analysis yields a rich
repertoire of complex-cell properties. Proc. Early Cognitive Vision
Workshop, Isle Of Skye, Scotland, May 28-June 1.



Blaschke, T. and Wiskott, L. (2004). CuBICA: Independent Component
Analysis by Simultaneous Third- and Fourth-Order Cumulant Diagonalization.
IEEE Transactions on Signal Processing, 52(5):1250-1256.



Blaschke, T. and Wiskott, L. (2004). Independent Slow Feature Analysis and
Nonlinear Blind Source Separation. Proc. 5th Int'l Conf. on Independent
Component Analysis and Blind Signal Separation (ICA'04), Granade, September
22-24, in series Lecture Notes in Computer Science, publ. Springer.



2003



Berkes, P. and Wiskott, L. (2003). Slow feature analysis yields a rich
repertoire of complex-cell properties. Cognitive Sciences EPrint Archive
(CogPrints) 2804, http://cogprints.org/2804/.



Berkes, P. and Wiskott, L. (2003). Slow feature analysis yields a rich
repertoire of complex-cell properties. Proc. 29th Göttingen Neurobiology
Conference, Göttingen, June 12-15 (abstract).



Blaschke, T. and Wiskott, L. (2003). CuBICA: Independent Component
Analysis by Simultaneous Third- and Fourth-Order Cumulant Diagonalization.
Computer Science Preprint Server (CSPS): Computational
Intelligence/0304002,
http://www.compscipreprints.com/comp/Preprint/blaschke/20030409/1/.



Wiskott, L. (2003). Estimating Driving Forces of Nonstationary Time Series
with Slow Feature Analysis. arXiv.org e-Print archive,
http://arxiv.org/abs/cond-mat/0312317.



Wiskott, L. (2003). How Does Our Visual System Achieve Shift and Size
Invariance? Cognitive Sciences EPrint Archive (CogPrints) 3321,
http://cogprints.org/3321/, (review) (a reprint of Wisk2006a).



Wiskott, L. (2003). Slow Feature Analysis: A Theoretical Analysis of
Optimal Free Responses. Neural Computation, 15(9):2147-2177.



Wiskott, L. and Berkes, P. (2003). Is Slowness a Learning Principle of the
Visual Cortex? Proc. Jahrestagung der Deutschen Zoologischen Gesellschaft
2003, Berlin, June 9-13, special issue of Zoology, 106(4):373-382.



2002



Berkes, P. and Wiskott, L. (2002). Applying Slow Feature Analysis to Image
Sequences Yields a Rich Repertoire of Complex Cell Properties. Proc. Int'l
Conf. on Artificial Neural Networks, ICANN'02, Madrid, August 27-30,
ed. José R. Dorronsoro, in series Lecture Notes in Computer Science,
publ. Springer-Verlag, pp. 81-86.



Blaschke, T. and Wiskott, L. (2002). An Improved Cumulant Based Method for
Independent Component Analysis. Proc. Int'l Conf. on Artificial Neural
Networks, ICANN'02, Madrid, August 27-30, ed. José R. Dorronsoro, in series
Lecture Notes in Computer Science, publ. Springer-Verlag, pp. 1087-1093.



Wiskott, L. and Berkes, P. (2002). Is slowness a principle for the
emergence of complex cells in primary visual cortex? Proc. Berlin
Neuroscience Forum 2002, Liebenwalde, April 18-20, ed. Helmut Kettenmann,
publ. Max-Delbrück-Centrum für Molekulare Medizin (MDC), Berlin, p. 43,
(abstract).



Wiskott, L. and Sejnowski, T.J. (2002). Slow Feature Analysis:
Unsupervised Learning of Invariances. Neural Computation, 14(4):715-770.



2001



Wiskott, L. (2001). Unsupervised Learning of Invariances in a Simple Model
of the Visual System. Proc. The Mathematical, Computational and Biological
Study of Vision, Oberwolfach, November 4-10, eds. D. Mumford, J.-M. Morel,
and C. von der Malsburg, publ. Mathematisches Forschungsinstitut
Oberwolfach, Report 49/2001, pp. 21-22, (abstract).


Last updated on 2022-08-09 at 01:07