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Spatially distributed computation in cortical circuits

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journal contribution
posted on 06.05.2022, 09:38 authored by Sergei Gepshtein, Ambarish S Pawar, Sunwoo Kwon, Sergey SavelievSergey Saveliev, Thomas D Albright
The traditional view of neural computation in the cerebral cortex holds that sensory neurons are specialized, i.e., selective for certain dimensions of sensory stimuli. This view was challenged by evidence of contextual interactions between stimulus dimensions in which a neuron’s response to one dimension strongly depends on other dimensions. Here, we use methods of mathematical modeling, psychophysics, and electrophysiology to address shortcomings of the traditional view. Using a model of a generic cortical circuit, we begin with the simple demonstration that cortical responses are always distributed among neurons, forming characteristic waveforms, which we call neural waves. When stimulated by patterned stimuli, circuit responses arise by interference of neural waves. Results of this process depend on interaction between stimulus dimensions. Comparison of modeled responses with responses of biological vision makes it clear that the framework of neural wave interference provides a useful alternative to the standard concept of neural computation.

Funding

Neuromorphic memristive circuits to simulate inhibitory and excitatory dynamics of neuron networks: from physiological similarities to deep learning

Engineering and Physical Sciences Research Council

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Salk Institute’s Sloan-Swartz Center for Theoretical Neurobiology

Kavli Institute for Brain and Mind

Conrad T. Prebys Foundation

NIH (R01-EY018613 and R01-EY029117)

History

School

  • Science

Department

  • Physics

Published in

Science Advances

Volume

8

Issue

16

Publisher

American Association for the Advancement of Science (AAAS)

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by American Association for the Advancement of Science (AAAS) under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

03/03/2022

Publication date

2022-04-22

Copyright date

2022

eISSN

2375-2548

Language

en

Depositor

Prof Sergey Saveliev. Deposit date: 4 May 2022

Article number

eabl5865

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