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Quantifying Information Conveyed by Large Neuronal Populations.

John A Berkowitz¹, Tatyana O Sharpee²

¹Department of Physics, University of California San Diego, San Diego, CA 92093, U.S.A. jaberkow@ucsd.edu.

Neural Computation

|April 14, 2019

Summary

This summary is machine-generated.

This study presents a novel method to compute mutual information in large neural circuits. The approach effectively approximates complex calculations, making neural information processing more tractable for machine learning and neuroscience research.

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Area of Science:

Computational Neuroscience
Machine Learning
Information Theory

Background:

Quantifying mutual information in large neural circuits is crucial but computationally intractable.
Existing methods face challenges due to the exponential growth of terms for evaluation.

Purpose of the Study:

To develop an effective method for computing mutual information in large neural populations.
To approximate mutual information using lower-dimensional conditional terms.

Main Methods:

Approximating individual neuron input-output functions using a logistic function.
Modeling neural responses sensitive to multiple stimulus components.
Decomposing mutual information into a sum of lower-dimensional conditional mutual information terms.

Main Results:

The proposed method effectively computes mutual information in large neural populations.
Approximations become exact in the limit of large populations and specific receptive field distributions.
Empirical results show good performance even when conditions are not strictly met.

Conclusions:

The developed approximation offers a computationally efficient way to quantify information in neural circuits.
This method advances the understanding of information processing in complex neural systems.
The linear growth in computational cost with input dimension is favorable compared to other methods.