Publications

Journal papers - Peer-reviewed - Conferences and pre-prints

Journal papers

  1. Berkes, P., Orban, G., Lengyel, M., and Fiser, J. (2011).
    Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment.
    Science, 331:6013, 83–87.
    (abstract, reprint, full text, supplementary material)
  2. Shelton, J., Bornschein, J., Sheikh, A.S., Berkes, P., Lucke, J. (2011).
    Select and Sample - A Model of Efficient Neural Inference and Learning
    Advances in Neural Information Processing Systems, 24, pp. 2618-2626.
    (abstract, paper.pdf)
  3. Wilbert, N., Zito, T., Schuppner, R.B., Jedrejewski-Szmek, Z., Wiskott, L., and Berkes, P. (2011)
    Building extensible frameworks for data processing: the case of MDP, Modular Toolkit for Data Processing
    Journal of Computational Science
  4. March, M.C., Trotta, R., Berkes P., Starkman, G.D., Vaudrevange, P.M. (2011)
    Improved constraints on cosmological parameters from SNIa data.
    Monthly Notices of the Royal Astronomical Society. (in press)
    (link to pre-print)
  5. Wiskott, L., Berkes, P., Franzius, M., Sprekeler, H., and Wilbert, N. (2011)
    Slow Feature Analysis.
    Scholarpedia , 6(4):5282.
  6. Fiser, J., Berkes, P., Orban, G, and Lengyel, M. (2010).
    Statistically optimal perception and learning: from behavior to neural representations
    Trends in Cognitive Sciences, 14:3, 119-130.
    (link to paper)
  7. 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)
  8. Berkes, P., White, B.L., and Fiser, J. (2009)
    No evidence for active sparsification in the visual cortex
    Advances in Neural Information Processing Systems, 22.
    (paper.pdf, supplementary material, poster.pdf)
  9. 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, Matlab demo
  10. 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
  11. Berkes, P., Turner, R. and Sahani, M. (2008).
    On sparsity and overcompleteness in image models.
    Advances in Neural Information Processing Systems, 20.
    (paper.pdf) - Project page
  12. 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
  13. 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.
    (link to paper, paper.pdf)
  14. 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
  15. 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.
    (link to paper) - Additional on-line material - Matlab source code
  16. Wiskott, L. and Berkes, P. (2003).
    Is slowness a learning principle of visual cortex?
    Zoology, 106(4):373-382.
    (link to paper)

Other peer-reviewed publications:

  1. 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. (paper.pdf)
  2. 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
    (abstract, paper.ps) - Additional on-line material to this paper

Conference contributions and preprints

  1. Savin, C., Berkes, P., Chiayu, C., Fiser, J., Lengyel, M. (2012).
    Similarity between spontaneous and sensory-evoked activity does suggest learning in the cortex.
    Computational and Systems Neuroscience, 2013.
  2. Haefner, R., Berkes, P., Fiser, J. (2012).
    Perceptual decision-making in a sampling-based neural representation.
    Computational and Systems Neuroscience, 2013.
  3. Luecke, J., Shelton, J.A., Sterne, P., Bornschein, J., Berkes, P., Sheikh A.-S. (2013).
    Combining feed-forward processing and sampling for neurally plausible encoding models
    Computational and Systems Neuroscience, 2013.
  4. Haefner, R., Berkes, P., Fiser, J. (2012).
    Decision-making and attention in a sampling-based neural representation.
    Computational and Systems Neuroscience, 2012.
  5. Haefner R., Berkes, P., Fiser, J. (2012).
    Decision-making in a sampling-based neural representation.
    Frontiers in Neuroscience. Conference Abstract: Neural Coding, Decision-Making & Integration in Time.
    (abstract)
  6. Marisa, M.C., Trotta, R., Berkes P., Starkman, G.D., Vaudrevange, P.M. (2011).
    A New Method For Cosmological Parameter Estimation From SNIa Data
    American Astronomical Society, AAS Meeting #217, #214.05
    Bulletin of the American Astronomical Society, Vol. 43, 2011
  7. Berkes, P., Fiser, J. (2011)
    A frequentist two-sample test based on Bayesian model selection
    arXiv:1104.2826
  8. March, M.C., Trotta, R., Berkes P., Starkman, G.D., Vaudrevange, P.M. (2011)
    Improved constraints on cosmological parameters from SNIa data
    arXiv:1102.3237
  9. Berkes, P., Turner, R., Fiser, J. (2011)
    The army of one (sample): the characteristics of sampling-based probabilistic neural representations
    Frontiers in Neuroscience. Conference Abstract: Computational and Systems Neuroscience 2011.
    (poster.pdf)
  10. Turner, R., Berkes, P., Fiser, J. (2011)
    Learning complex tasks with probabilistic population codes
    Frontiers in Neuroscience. Conference Abstract: Computational and Systems Neuroscience 2011.
    (poster.pdf)
  11. Berkes, P., David, S.V., Fritz, J., Shamma, S.A., and Fiser, J. (2010)
    Neural activity as samples from a probabilistic representation: evidence from the auditory cortex Frontiers in Neuroscience. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00140
    (abstract, poster.pdf) - Project page
  12. Berkes, P., White, B.L., and Fiser J. (2010)
    Sparseness is not actively optimized in V1
    Frontiers in Neuroscience. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00310
    (abstract, poster.pdf)
  13. Berkes, P., Orban, G., Lengyel, M., and Fiser, J. (2009)
    Statistically optimal learning revealed by the development of spontaneous and evoked activity in the primary visual cortex
    Society for Neuroscience meeting (SfN), Chicago (abstract).
  14. 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).
  15. 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).
  16. 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
  17. 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, Matlab demo
  18. 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
  19. 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)
  20. 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
  21. 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
  22. 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, code.zip
  23. 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)
  24. 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)
  25. 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)
  26. 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)
  27. Berkes, P. and Zito, T. (2006).
    MDP 2.0 - A data processing framework for scientific development and education.
    Europython 2006, (abstract).
    (abstract) - MDP homepage
  28. 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)
  29. Berkes, P. and Zito, T. (2005).
    Modular toolkit for Data Processing (MDP).
    Europython 2005, (abstract).
    (abstract) - MDP homepage
  30. 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)
  31. 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)
  32. 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
  33. 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
  34. 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)
  35. 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
  36. 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)
  37. 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. Linking Bayesian models of perception and neural responses with spontaneous activity Center for Brain Science, Harvard University, February 2011.
  2. Linking Bayesian models of perception and neural responses with spontaneous activity Laboratory of Computational Neuroscience, EPFL, Lausanne, January 2011.
  3. Spontaneous neural activity reveals optimal internal models of the environment Institute for Theoretical Biology, Humboldt Universitaet zu Berlin, Berlin, July 2010.
  4. Generative models of vision: from sparse coding toward structured models Redwood Institute for Theoretical Neuroscience, Berkeley, December 2009.
  5. Neural evidence for optimal inference and learning in primary visual cortex Redwood Institute for Theoretical Neuroscience, Berkeley, December 2009.
  6. Optimal inference and learning in the visual cortex: Models and neural evidence. EPFL, Lausanne, June 2009.
  7. Beyond correlations: modeling neural dependencies with copulas NIPS workshop on "Statistical analysis and modeling of response dependencies in neural populations", Whistler, December 2008.
  8. Generative models predict the relation between evoked and spontaneous activity. Phd/Postdoc symposium at BCCN conference, Munich, October 2008.
  9. On Sparsity and Overcompleteness in Image Models. Inference Group, Cambridge, UK, April 2008.
  10. Structured representations in the visual cortex. Workshop on generative models in vision, Budapest, June 2007.
  11. Simple and complex cells in a model of content/style structure. NISA Workshop on Feature Learning, Copenhagen, September 2006.