NIPS workshop

WORKSHOPS NIPS*96

Population coding: interpreting the responses of large neuronal populations



Goal

Neurophysiological recordings have clearly established that sensory variables, such as the color of an object, and motor variables, such as the direction of an arm movement, are encoded in the activities of large populations of neurons. The nature of the code, however, remains elusive. One classical approach treats the response of a single cell as the output of a deterministic function of an input variable, but corrupted by noise. Within this formulation, reading out the population activity can be approached from the perspective of estimation theory, using methods such as the optimum linear estimator or maximum likelihood. By contrast, more recent approaches interpret the neuronal responses as encoding whole probability distributions over the variables. This provides a natural way of encoding certainty and variance and of performing Bayesian inferences over the variables. Other recent investigations have focused on the role of noise and methods of handling noise in these representations, as well as methods of determining the dimensions underlying a population code.
The goal of the workshop is to present recent work that has focused on these issues of population code interpretations, and to examine relevant experimental evidence. Emphasis will be put on work in progress but all speakers will also be asked to address a set of common issues including:

1- Are the classical and probabilistic approaches incompatible and/or how do we go from one to the other?

2- What are the evidence for probabilistic formulations and how can they be tested empirically?

3- Are biologically plausible decoding schemes necessarily sub-optimal?

4- Can we identify the sources of noise in neurons and understand how neuronal circuits deal with them?

5- How can feed-forward and lateral connections operate in cleaning up noisy representations?

6- How can we reverse-engineer population codes to reveal the underlying variables, and the relevant coordinate system(s), that are being represented?
Program:

Morning (7-10am):

Mike Shadlen
Signal, Noise, Synchrony and Redundancy: What do Cortical Neurons Tell Each Other?

Terry Sanger
Implementation of Common Network Learning Algorithms in Populations of Spiking Neurons

Herman Snippe
Two-Stage Maximum Likelihood Methods for Decoding Population Responses

Alexandre Pouget
Lateral Connections and Population Coding


Afternoon (4-7pm):

David Redish
Coherency: Measuring the Quality of a Distributed Neural Code

Charlie Anderson
Neuronal Ensembles as Encoding and Processing Probability Density Functions (PDFs)

Rich Zemel
Probabilistic Interpretation of Population Code Representations

Larry Abbott
Is the Information in Population Codes Carried by Slowly Firing Neurons?

Simon Thorpe
Rank Order Coding


Click here for abstracts

Format:
One day workshop (Friday, 6th of December)
Mini-conference/group discussion

Recommended review paper:

Salinas, E.; Abbot, L.F. Vector reconstruction from firing rates. Journal of Computational Neuroscience. 1:89-107. 1994.

Other relevant publications:

Paradiso, M.A. A theory of the use of visual orientation information which exploits the columnar structure of striate cortex. Biological Cybernetics. 58:35-49. 1988.

Pouget, A. and Thorpe, S.J. Connectionist models of orientation identification. Connection Science. 3(2):127-142. 1991.

Seung, H.S. and Sompolinsky, H. Simple model for reading neuronal population codes. Proceedings of National Academy of Sciences. USA. 90:10749-10753. 1993.

Anderson, C.H. Basic elements of biological computational systems. International Journal of Modern Physics C135-137. 5(2). 1994.

Sanger, T.D. Theoretical consideration for the analysis of population coding in motor cortex. Neural Computation. 6:29-37. 1994.

Zemel, R. and Hinton, G. Learning population codes by minimizing description length. Neural Computation. 7(3):549-564. 1995.

Shadlen, M.N.; Britten, K.H.; Newsome, W.T.; Movshon, T.A. A computational analysis of the relationship between neuronal and behavioral responses to visual motion. Journal of Neuroscience. 16(4):1486-1510. 1996.

Snippe, H.P. Parameter extraction from population codes: a critical assessment. Neural Computation. 8(3):511-530. 1996.


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