Memoryless Optimality: Neurons Do Not Need Adaptation to Optimally Encode Stimuli With Arbitrarily Complex Statistics

Oren Forkosh*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Our neurons seem capable of handling any type of data, regardless of its scale or statistical properties. In this letter, we suggest that optimal coding may occur at the single-neuron level without requiring memory, adaptation, or evolutionary-driven fit to the stimuli. We refer to a neural circuit as optimal if it maximizes the mutual information between its inputs and outputs. We show that often encountered differentiator neurons, or neurons that respond mainly to changes in the input, are capable of using all their information capacity when handling samples of any statistical distribution. We demonstrate this optimality using both analytical methods and simulations. In addition to demonstrating the simplicity and elegance of neural processing, this result might provide a way to improve the handling of data by artificial neural networks.

Original languageAmerican English
Article number12
Pages (from-to)2374-2387
Number of pages14
JournalNeural Computation
Volume34
Issue number12
DOIs
StatePublished - Dec 2022

Bibliographical note

Publisher Copyright:
© 2022 Massachusetts Institute of Technology.

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