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Decoding Natural Behavior from Neuroethological Embedding

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Efficient sensory encoding and Bayesian inference with heterogeneous neural populations.

Deep Ganguli¹, Eero P Simoncelli

¹Howard Hughes Medical Institute, Center for Neural Science and Courant Institute of Mathematical Sciences, New York University, New York, NY 10003, U.S.A. dganguli@cns.nyu.edu.

Neural Computation

|July 25, 2014

Summary

Sensory systems efficiently encode environmental information by allocating more neurons to common stimuli. This neural coding optimizes information transmission and improves stimulus discrimination based on prior probabilities.

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Area of Science:

Neuroscience
Computational Neuroscience
Information Theory

Background:

The efficient coding hypothesis suggests sensory systems maximize information transmission.
Neural populations encode sensory variables using neurons with tuning curves.

Purpose of the Study:

Develop a testable model of efficient neural coding for sensory variables.
Investigate how prior probabilities influence optimal neural population structure and function.

Main Methods:

Parameterize neural populations with continuous functions for tuning curve density and amplitude.
Solve for information-maximizing allocation of tuning curves analytically.
Compute stimulus discrimination capabilities and derive a Bayesian population vector decoder.

Main Results:

Optimal cell density is proportional to the prior probability distribution.
More cells with narrower tuning encode high-probability stimuli.
Discrimination thresholds are inversely proportional to the sensory prior.

Conclusions:

Neural populations can optimally embed prior information for efficient sensory processing.
The Bayesian population vector decoder approximates Bayesian inference using neural population codes.
Results offer testable predictions for neural and perceptual data.