Conference: Computational Neuroscience Meeting 2009

from: 2009, 18 Jul - 09:00 to: 2009, 23 Jul - 18:00

Matthias Bethge, Jakob Macke, and Philipp Berens
Computational Vision and Neuroscience Group
MPI for Biological Cybernetics, Tübingen


Modern recording techniques such as multi-electrode arrays and 2-photon imaging are capable of simultaneously monitoring the activity of large neuronal ensembles at single cell resolution. This makes it possible to study the dynamics of neural populations of considerable size, and to gain insights into their computations and functional organization. The key challenge with multi-electrode recordings is their high-dimensional nature. Understanding this kind of data requires powerful statistical techniques for capturing the structure of the neural population responses and their relation with external stimuli or behavioural observations.

The goal of this workshop is to present recent advances in the statistical modelling of neural populations, and to discuss the following central questions:

  1. What classes of statistical methods are most useful for modelling population activity?
  2. What are the main limitations of current approaches, and what can be done to overcome them?
  3. How can statistical methods be used to empirically test existing models of (probabilistic) population coding?
  4. What role can statistical methods play in formulating novel hypotheses about the principles of information processing in neural populations?

Speakers (* speaker confirmed)

  • Andrea Benucci* (Institute of Ophtalmology, University College London): Decoding sequences of population responses in visual cortex
  • Jakob Macke* (Computational Vision and Neuroscience Group, MPI for Biological Cybernetics, Tübingen): Modelling correlated populations - Redundancies, spike counts and the effect of common input
  • Bruno Olshausen* (Helen Wills Neuroscience Institute and School of Optometry, UC Berkeley, and Redwood Center for Theoretical Neuroscience): How do populations in V1 respond to natural scenes?
  • Liam Paninski* (Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York): Common input and optimal decoding in the retina
  • Stefano Panzeri* (Italian Institute of Technology, Genoa): The impact of high-order correlation on the information about whisker kinetics encoded by rat barrel cortex neurons
  • Maneesh Sahani* (Gatsby Computational Neuroscience Unit, University College London): Gaussian process methods for single-trial population analysis of dynamical data
  • Jonathan Victor* (Cornell Weill Medical College): Modelling response properties of V1 neurons: The effect of higher-order correlations.
  • Peter Latham (Gatsby Computational Neuroscience Unit, University College London): What can we infer about the statistics of neuronal activity in large populations from measurements on small ones?


  • Matteo

    Carandini, Jonathan B Demb, Valerio Mante, Bruno A Olshausen, David J

    Tolhurst, Yang Dan, Jack L Gallant, and Nicole Rust "Do we know what the

    early visual system does?" J Neurosci, 25, no. 46 (2005)

  • Mark M

    Churchland, Byron M Yu, Maneesh Sahani, Krishna V Shenoy “Techniques

    for extracting single-trial activity patterns from large-scale neural

    recordings,” Current Opinion in Neurobiology 17, no. 5 (10/2007)

  • Jonathan

    W. Pillow, Jonathon Shlens, Liam Paninski, Alexander Sher, Alan M

    Litke, EJ Chichilnisky, Eeero P Simoncelli, “Spatio-temporal

    correlations and visual signalling in a complete neuronal population,”

    Nature no. 454, (7/2008)

  • Liam Paninski, Jonathan W Pillow,

    Jeremi Lewi, “Statistical models for neural encoding, decoding, and

    optimal stimulus design.” In Computational Neuroscience: Theoretical

    Insights Into Brain Function, eds. P Cisek, T Drew, & J Kalaska


  • Stefano Panzeri, Gianni Pola, and Rasmus S. Petersen,

    “Coding of Sensory Signals by Neuronal Populations: The Role of

    Correlated Activity,” Neuroscientist 9, no. 3 (6/2003)


Location: Berlin
  • Werner Reichardt Centre for Integrative Neuroscience
  • Max Planck Institute for Biological Cybernetics