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Robust parallel decision-making in neural circuits with nonlinear inhibition
An elemental computation in the brain is to identify the best in a set of options and report its value. It is required for inference, decision-making, optimization, action selection, consensus, and foraging. Neural computing is considered powerful because of its parallelism; however, it is unclear w...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568288/ https://www.ncbi.nlm.nih.gov/pubmed/33008882 http://dx.doi.org/10.1073/pnas.1917551117 |
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author | Kriener, Birgit Chaudhuri, Rishidev Fiete, Ila R. |
author_facet | Kriener, Birgit Chaudhuri, Rishidev Fiete, Ila R. |
author_sort | Kriener, Birgit |
collection | PubMed |
description | An elemental computation in the brain is to identify the best in a set of options and report its value. It is required for inference, decision-making, optimization, action selection, consensus, and foraging. Neural computing is considered powerful because of its parallelism; however, it is unclear whether neurons can perform this max-finding operation in a way that improves upon the prohibitively slow optimal serial max-finding computation (which takes [Formula: see text] time for [Formula: see text] noisy candidate options) by a factor of [Formula: see text] , the benchmark for parallel computation. Biologically plausible architectures for this task are winner-take-all (WTA) networks, where individual neurons inhibit each other so only those with the largest input remain active. We show that conventional WTA networks fail the parallelism benchmark and, worse, in the presence of noise, altogether fail to produce a winner when [Formula: see text] is large. We introduce the nWTA network, in which neurons are equipped with a second nonlinearity that prevents weakly active neurons from contributing inhibition. Without parameter fine-tuning or rescaling as [Formula: see text] varies, the nWTA network achieves the parallelism benchmark. The network reproduces experimentally observed phenomena like Hick’s law without needing an additional readout stage or adaptive [Formula: see text]-dependent thresholds. Our work bridges scales by linking cellular nonlinearities to circuit-level decision-making, establishes that distributed computation saturating the parallelism benchmark is possible in networks of noisy, finite-memory neurons, and shows that Hick’s law may be a symptom of near-optimal parallel decision-making with noisy input. |
format | Online Article Text |
id | pubmed-7568288 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-75682882020-10-27 Robust parallel decision-making in neural circuits with nonlinear inhibition Kriener, Birgit Chaudhuri, Rishidev Fiete, Ila R. Proc Natl Acad Sci U S A Biological Sciences An elemental computation in the brain is to identify the best in a set of options and report its value. It is required for inference, decision-making, optimization, action selection, consensus, and foraging. Neural computing is considered powerful because of its parallelism; however, it is unclear whether neurons can perform this max-finding operation in a way that improves upon the prohibitively slow optimal serial max-finding computation (which takes [Formula: see text] time for [Formula: see text] noisy candidate options) by a factor of [Formula: see text] , the benchmark for parallel computation. Biologically plausible architectures for this task are winner-take-all (WTA) networks, where individual neurons inhibit each other so only those with the largest input remain active. We show that conventional WTA networks fail the parallelism benchmark and, worse, in the presence of noise, altogether fail to produce a winner when [Formula: see text] is large. We introduce the nWTA network, in which neurons are equipped with a second nonlinearity that prevents weakly active neurons from contributing inhibition. Without parameter fine-tuning or rescaling as [Formula: see text] varies, the nWTA network achieves the parallelism benchmark. The network reproduces experimentally observed phenomena like Hick’s law without needing an additional readout stage or adaptive [Formula: see text]-dependent thresholds. Our work bridges scales by linking cellular nonlinearities to circuit-level decision-making, establishes that distributed computation saturating the parallelism benchmark is possible in networks of noisy, finite-memory neurons, and shows that Hick’s law may be a symptom of near-optimal parallel decision-making with noisy input. National Academy of Sciences 2020-10-13 2020-10-02 /pmc/articles/PMC7568288/ /pubmed/33008882 http://dx.doi.org/10.1073/pnas.1917551117 Text en Copyright © 2020 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Biological Sciences Kriener, Birgit Chaudhuri, Rishidev Fiete, Ila R. Robust parallel decision-making in neural circuits with nonlinear inhibition |
title | Robust parallel decision-making in neural circuits with nonlinear inhibition |
title_full | Robust parallel decision-making in neural circuits with nonlinear inhibition |
title_fullStr | Robust parallel decision-making in neural circuits with nonlinear inhibition |
title_full_unstemmed | Robust parallel decision-making in neural circuits with nonlinear inhibition |
title_short | Robust parallel decision-making in neural circuits with nonlinear inhibition |
title_sort | robust parallel decision-making in neural circuits with nonlinear inhibition |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568288/ https://www.ncbi.nlm.nih.gov/pubmed/33008882 http://dx.doi.org/10.1073/pnas.1917551117 |
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