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Publications

Journal papers

  1. Berkes, P., Turner, R.E., and Sahani, M. (2009).
    A Structured Model of Video Reproduces Primary Visual Cortical Organisation
    PLoS Computational Biology, 5(9): e1000495. doi:10.1371/journal.pcbi.1000495 .
    (link to paper)
  2. Zito, T., Wilbert, N., Wiskott, L., and Berkes, P. (2009).
    Modular toolkit for Data Processing (MDP): a Python data processing framework.
    Frontiers in Neuroinformatics (2008) 2:8. doi:10.3389/neuro.11.008.2008
    (link to paper) - MDP homepage
  3. Berkes, P. and Wiskott, L. (2007).
    Analysis and interpretation of quadratic models of receptive fields.
    Nature Protocols, 2:2, 400-407.
    (link to paper) Additional on-line material - Matlab source code
  4. Blaschke, T., Berkes, P. and Wiskott, L. (2006).
    What is the relation between slow feature analysis and independent component analysis?
    Neural Computation, 18:10, 2495-2508.
    (abstract, .pdf) - Project page by L. Wiskott
  5. Berkes, P. and Wiskott, L. (2006).
    On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields.
    Neural Computation, 18:8, 1868-1895.
    (link to paper) - Additional on-line material - Matlab source code
  6. Berkes, P. and Wiskott, L. (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.
    (abstract, link to paper) - Additional on-line material - Matlab source code

Other peer-reviewed publications:

  1. Berkes, P., Wood, F., and Pillow, J. (2009).
    Characterizing neural dependencies with copula models
    Advances in Neural Information Processing Systems, 21:119-136.
    (paper.pdf, poster.pdf) - Project page
  2. Turner, R., Berkes, P., and Sahani, M. (2008).
    Two problems with variational Expectation Maximisation for time-series models
    Proc. Inference and Estimation in Probabilistic Time-Series Models Workshop, Cambridge. (.pdf)
  3. Berkes, P., Turner, R. and Sahani, M. (2008).
    On sparsity and overcompleteness in image models.
    Advances in Neural Information Processing Systems, 20.
    (.pdf) - Project page
  4. Wiskott, L. and Berkes, P. (2003).
    Is Slowness a Learning Principle of Visual Cortex?
    Proc. Jahrestagung der Deutschen Zoologischen Gesellschaft 2003, Berlin, June 9-13,
    special issue of Zoology, 106(4):373-382.
    (bibtex, abstract)
  5. Berkes, P. and Wiskott, L. (2002).
    Applying Slow Feature Analysis to Image Sequences Yields a Rich Repertoire of Complex Cell Properties.
    in Artificial Neural Networks - ICANN 2002,
    ed. Jose R. Dorronsoro, Springer Verlag, pp. 81-86
    (bibtex, abstract, .ps) - Additional on-line material to this paper

Conference contributions and preprints

  1. Berkes, P., Orban, G., Lengyel, M. and Fiser, J. (2009)
    Neural evidence for statistically optimal inference and learning in primary visual cortex
    Sloan-Swartz Centers for Theoretical Neurobiology Annual Meeting, Boston (abstract).
  2. Cui, M., Orban, G., Berkes, P., and Fiser, J. (2009)
    What eye-movements tell us about online learning of the structure of scenes.
    Vision Science Society meeting 2009 (abstract).
  3. Berkes, P., Orban, G., Lengyel, M., and Fiser, J. (2009).
    Matching spontaneous and evoked activity in V1: a hallmark of probabilistic inference.
    Frontiers in Systems Neuroscience. Conference Abstract: Computational and systems neuroscience. doi: 10.3389/conf.neuro.06.2009.03.314
    (abstract.pdf) - Project page
  4. Berkes, P., Wood, F., and Pillow, J. (2008).
    Modeling neural dependencies with Poisson copulas.
    Frontiers in Computational Neuroscience. Conference Abstract: Bernstein Symposium 2008. doi: 10.3389/conf.neuro.10.2008.01.031 - Project page
  5. Orban G., Berkes, P., Lengyel, M., and Fiser J. (2008).
    Relating evoked and spontaneous cortical activities in a generative modeling framework.
    Sloan-Swartz Meeting of Theoretical Neurobiology, Princeton, NJ, USA
  6. Turner, R.E., Berkes, P., and Sahani, M. (2008).
    Two problems with variational Expectation Maximisation in timeseries models.
    Technical Report GCNU-TR-2008-001, Gatsby Computational Neuroscience Unit, UCL.
    (paper.pdf)
  7. Turner, R., Berkes, P., and Sahani, M. (2008).
    Finding the optimal sparse, overcomplete model for natural images by model selection.
    Cosyne 2008, Salt Lake City (abstract).
    (abstract.pdf, poster.pdf) - Project page
  8. Orban, G., Berkes, P., Lengyel, M., and Fiser, J. (2008).
    Looking for hallmarks of generative models in the visual cortex.
    Cosyne 2008, Salt Lake City (abstract).
    (abstract.pdf, poster.pdf) - Project page
  9. Berkes, P., Pillow, J., and Wood, F. (2008).
    Characterizing neural dependencies with Poisson copula models.
    Cosyne 2008, Salt Lake City (abstract).
    (abstract.pdf, poster.pdf) - Project page
  10. Berkes, P., Turner, R., and Sahani, M. (2007)
    Complex and simple cells are identity and attribute variables in a generative model of natural images.
    Proc. 39th Annual European Brain and Behaviour Society, Trieste, Italy, eds. Alessandro Treves et al., special issue of Neural Plasticity, Article ID 23250, p. 30 (abstract).
    (link to journal)
  11. Wiskott, L., Franzius, M., Berkes, P., and Sprekeler, H. (2007)
    Is slowness a learning principle of the visual system?
    Proc. 39th Annual European Brain and Behaviour Society, Trieste, Italy, eds. Alessandro Treves et al., special issue of Neural Plasticity, Article ID 23250, pp. 14-15 (abstract).
    (link to journal)
  12. Berkes, P., Turner, R. and Sahani, M. (2007).
    Simple and complex cells as style and content variables in a bilinear model based on temporal stability.
    Cosyne 2007, Salt Lake City, II-111 (abstract).
    (abstract.pdf, poster.pdf)
  13. Wiskott, L., Sprekeler, H., and Berkes, P. (2007).
    Towards an analytical derivation of complex cell receptive field properties.
    Proc. 7th Meeting of the German Neuroscience Society - 31st Goettingen Neurobiology Conference, Goettingen, S12-2 (abstract).
    (bibtex, abstract)
  14. Berkes, P. and Zito, T. (2006).
    MDP 2.0 - A data processing framework for scientific development and education.
    Europython 2006, (abstract).
    (abstract) - MDP homepage
  15. Berkes, P. (2005).
    Handwritten digit recognition with Nonlinear Fisher Discriminant Analysis.
    Proc. of ICANN Vol. 2, Springer, LNCS 3696, 285-287.
    (abstract.pdf, presentation: .sxi, .ppt.gz, .pdf)
  16. Berkes, P. and Zito, T. (2005).
    Modular toolkit for Data Processing (MDP).
    Europython 2005, (abstract).
    (abstract) - MDP homepage
  17. Berkes, P. (2005).
    Pattern recognition with Slow Feature Analysis.
    Cognitive Sciences EPrint Archive (CogPrint) 4104, http://cogprints.org/4104/
    (<add date of your document download here>).
    (.ps,.pdf)
  18. 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).
    (bibtex, abstract)
  19. Berkes, P. and Wiskott, L. (2005).
    On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields.
    Cognitive Sciences EPrint Archive (CogPrint) 4081, http://cogprints.org/4081/
    (<add date of your document download here>).
    (bibtex, abstract, .ps,.pdf) - Additional on-line material to this paper - Matlab source code
  20. 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.
    (bibtex, abstract, .pdf) - Additional on-line material to this paper
  21. Berkes, P. and Wiskott, L. (2003).
    Slow feature analysis yields a rich repertoire of complex-cell properties.
    Proc. 29th Goettingen Neurobiology Conference, Goettingen, June 12-15.
    (bibtex, abstract, poster .pdf)
  22. Berkes, P. and Wiskott, L. (2003).
    Slow feature analysis yields a rich repertoire of complex-cell properties.
    Cognitive Sciences EPrint Archive (CogPrint) 2804, http://cogprints.org/2804/
    (<add date of your document download here>).
    (bibtex, abstract, .ps,.pdf) - Additional on-line material to this paper
  23. 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-Delbrueck-Centrum fuer Molekulare Medizin (MDC), Berlin, p. 43.
    (bibtex, abstract)
  24. A. Unterkircher, P. Berkes and J. Reissner (2001).
    An efficient algorithm for parallel stiffness matrix assembling on shared memory machines.
    Simulation of Materials Processing: Theory, Methods and Applications
    Proc. NUMIFORM 2001

Thesis:

Berkes, P. (2006)
Temporal slowness as an unsupervised learning principle - self-organization of complex-cell receptive fields and application to pattern recognition.
PhD Thesis, electronically published at http://edoc.hu-berlin.de/, urn:nbn:de:kobv:11-10058759.
Institute for Theoretical Biology (ITB), Berlin.
Supervisor: Laurenz Wiskott
(abstract and full text)

Berkes, P. (2001)
Learning of disparity selective neurons from natural images.
Diploma Thesis, Institute for Neuroinfomatics (INI), Zurich.
Supervisors: Konrad Koerding, Peter Koenig

Invited presentations:

  1. Beyond correlations: modeling neural dependencies with copulas NIPS workshop on "Statistical analysis and modeling of response dependencies in neural populations", Whistler, December 2008.
  2. Generative models predict the relation between evoked and spontaneous activity. Phd/Postdoc symposium at BCCN conference, Munich, October 2008.
  3. On Sparsity and Overcompleteness in Image Models. Inference Group, Cambridge, UK, April 2008.
  4. Structured representations in the visual cortex. Workshop on generative models in vision, Budapest, June 2007.
  5. Simple and complex cells in a model of content/style structure. NISA Workshop on Feature Learning, Copenhagen, September 2006.