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Excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice

We are constantly faced with decisions between alternatives defined by multiple attributes, necessitating an evaluation and integration of different information sources. Time-varying signals in multiple brain areas are implicated in decision-making; but we lack a rigorous biophysical description of...

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Autores principales: Pettine, Warren Woodrich, Louie, Kenway, Murray, John D., Wang, Xiao-Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7987200/
https://www.ncbi.nlm.nih.gov/pubmed/33705386
http://dx.doi.org/10.1371/journal.pcbi.1008791
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author Pettine, Warren Woodrich
Louie, Kenway
Murray, John D.
Wang, Xiao-Jing
author_facet Pettine, Warren Woodrich
Louie, Kenway
Murray, John D.
Wang, Xiao-Jing
author_sort Pettine, Warren Woodrich
collection PubMed
description We are constantly faced with decisions between alternatives defined by multiple attributes, necessitating an evaluation and integration of different information sources. Time-varying signals in multiple brain areas are implicated in decision-making; but we lack a rigorous biophysical description of how basic circuit properties, such as excitatory-inhibitory (E/I) tone and cascading nonlinearities, shape attribute processing and choice behavior. Furthermore, how such properties govern choice performance under varying levels of environmental uncertainty is unknown. We investigated two-attribute, two-alternative decision-making in a dynamical, cascading nonlinear neural network with three layers: an input layer encoding choice alternative attribute values; an intermediate layer of modules processing separate attributes; and a final layer producing the decision. Depending on intermediate layer E/I tone, the network displays distinct regimes characterized by linear (I), convex (II) or concave (III) choice indifference curves. In regimes I and II, each option’s attribute information is additively integrated. In regime III, time-varying nonlinear operations amplify the separation between offer distributions by selectively attending to the attribute with the larger differences in input values. At low environmental uncertainty, a linear combination most consistently selects higher valued alternatives. However, at high environmental uncertainty, regime III is more likely than a linear operation to select alternatives with higher value. Furthermore, there are conditions where readout from the intermediate layer could be experimentally indistinguishable from the final layer. Finally, these principles are used to examine multi-attribute decisions in systems with reduced inhibitory tone, leading to predictions of different choice patterns and overall performance between those with restrictions on inhibitory tone and neurotypicals.
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spelling pubmed-79872002021-04-02 Excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice Pettine, Warren Woodrich Louie, Kenway Murray, John D. Wang, Xiao-Jing PLoS Comput Biol Research Article We are constantly faced with decisions between alternatives defined by multiple attributes, necessitating an evaluation and integration of different information sources. Time-varying signals in multiple brain areas are implicated in decision-making; but we lack a rigorous biophysical description of how basic circuit properties, such as excitatory-inhibitory (E/I) tone and cascading nonlinearities, shape attribute processing and choice behavior. Furthermore, how such properties govern choice performance under varying levels of environmental uncertainty is unknown. We investigated two-attribute, two-alternative decision-making in a dynamical, cascading nonlinear neural network with three layers: an input layer encoding choice alternative attribute values; an intermediate layer of modules processing separate attributes; and a final layer producing the decision. Depending on intermediate layer E/I tone, the network displays distinct regimes characterized by linear (I), convex (II) or concave (III) choice indifference curves. In regimes I and II, each option’s attribute information is additively integrated. In regime III, time-varying nonlinear operations amplify the separation between offer distributions by selectively attending to the attribute with the larger differences in input values. At low environmental uncertainty, a linear combination most consistently selects higher valued alternatives. However, at high environmental uncertainty, regime III is more likely than a linear operation to select alternatives with higher value. Furthermore, there are conditions where readout from the intermediate layer could be experimentally indistinguishable from the final layer. Finally, these principles are used to examine multi-attribute decisions in systems with reduced inhibitory tone, leading to predictions of different choice patterns and overall performance between those with restrictions on inhibitory tone and neurotypicals. Public Library of Science 2021-03-11 /pmc/articles/PMC7987200/ /pubmed/33705386 http://dx.doi.org/10.1371/journal.pcbi.1008791 Text en © 2021 Pettine et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Pettine, Warren Woodrich
Louie, Kenway
Murray, John D.
Wang, Xiao-Jing
Excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice
title Excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice
title_full Excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice
title_fullStr Excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice
title_full_unstemmed Excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice
title_short Excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice
title_sort excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7987200/
https://www.ncbi.nlm.nih.gov/pubmed/33705386
http://dx.doi.org/10.1371/journal.pcbi.1008791
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