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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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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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.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1008791</identifier><identifier>PMID: 33705386</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Biology and Life Sciences ; Brain - physiology ; Cereals ; Channel gating ; Computational Biology ; Computer and Information Sciences ; Conductance ; Decision Making ; Glutamic acid receptors ; Glutamic acid receptors (ionotropic) ; Grocery stores ; Humans ; Linear transformations ; Memory ; Methods ; Model testing ; Models, Neurological ; N-Methyl-D-aspartic acid receptors ; Neural networks ; Neural Networks, Computer ; Physical Sciences ; Population ; Resistance ; Schizophrenia ; Short term memory ; Social Sciences ; Time constant ; Uncertainty</subject><ispartof>PLoS computational biology, 2021-03, Vol.17 (3), p.e1008791-e1008791</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Pettine et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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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.</description><subject>Analysis</subject><subject>Biology and Life Sciences</subject><subject>Brain - physiology</subject><subject>Cereals</subject><subject>Channel gating</subject><subject>Computational Biology</subject><subject>Computer and Information Sciences</subject><subject>Conductance</subject><subject>Decision Making</subject><subject>Glutamic acid receptors</subject><subject>Glutamic acid receptors (ionotropic)</subject><subject>Grocery stores</subject><subject>Humans</subject><subject>Linear transformations</subject><subject>Memory</subject><subject>Methods</subject><subject>Model testing</subject><subject>Models, Neurological</subject><subject>N-Methyl-D-aspartic acid receptors</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Physical Sciences</subject><subject>Population</subject><subject>Resistance</subject><subject>Schizophrenia</subject><subject>Short term memory</subject><subject>Social Sciences</subject><subject>Time constant</subject><subject>Uncertainty</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqVkktv1DAQxyMEoqXwDRBE4gKHLHYcP3JBqqoCK1Ug8ThbE2ey8ZLEW9sp7bfH291WXcQF-eDR-Df_eXiy7CUlC8okfb92s59gWGxMYxeUECVr-ig7ppyzQjKuHj-wj7JnIawJSWYtnmZHjEnCmRLH2dX5tbERovM3hZ1629itmUc3YR562GDIWzQ2WDflIXqIuLLJZ6cc8t6iB296a2DIJ5z97RV_O_8rH12LQ-66fJyHaAuI0dtmjpib3lmDz7MnHQwBX-zvk-znx_MfZ5-Li6-flmenF4URjMXCkEpIUdXI6lZIBhx4J1QjWNmCMLVssRKibKQyRikm67LBsmQCSmhqYIayk-z1TnczuKD3Iwu65JQJVVHCErHcEa2Dtd54O4K_0Q6svnU4v9LgozUDat6VLWnrCpQSFQcGTdUR6GpBKqMaJEnrwz7b3IzYGpzSxIYD0cOXyfZ65a60rJUsyVbg7V7Au8sZQ9SjDQaHASZ087ZuQktBOJcJffMX-u_uFjtqBakBO3Uu5TXptDhakz65s8l_KriSlBFWpYB3BwGJiXgdVzCHoJffv_0H--WQrXas8S4Ej939VCjR232-K19v91nv9zmFvXo40fuguwVmfwBrg_Ou</recordid><startdate>20210311</startdate><enddate>20210311</enddate><creator>Pettine, Warren Woodrich</creator><creator>Louie, Kenway</creator><creator>Murray, John D</creator><creator>Wang, Xiao-Jing</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-3124-8474</orcidid><orcidid>https://orcid.org/0000-0003-0063-0902</orcidid><orcidid>https://orcid.org/0000-0003-4115-8181</orcidid><orcidid>https://orcid.org/0000-0002-9665-5436</orcidid></search><sort><creationdate>20210311</creationdate><title>Excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice</title><author>Pettine, Warren Woodrich ; Louie, Kenway ; Murray, John D ; Wang, Xiao-Jing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c633t-c0467649e39d673a5a5f68b632da6c97de4662b78cc883792be2236a2ab9a3c13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Analysis</topic><topic>Biology and Life Sciences</topic><topic>Brain - physiology</topic><topic>Cereals</topic><topic>Channel gating</topic><topic>Computational Biology</topic><topic>Computer and Information Sciences</topic><topic>Conductance</topic><topic>Decision Making</topic><topic>Glutamic acid receptors</topic><topic>Glutamic acid receptors (ionotropic)</topic><topic>Grocery stores</topic><topic>Humans</topic><topic>Linear transformations</topic><topic>Memory</topic><topic>Methods</topic><topic>Model testing</topic><topic>Models, Neurological</topic><topic>N-Methyl-D-aspartic acid receptors</topic><topic>Neural networks</topic><topic>Neural Networks, Computer</topic><topic>Physical Sciences</topic><topic>Population</topic><topic>Resistance</topic><topic>Schizophrenia</topic><topic>Short term memory</topic><topic>Social Sciences</topic><topic>Time constant</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pettine, Warren Woodrich</creatorcontrib><creatorcontrib>Louie, Kenway</creatorcontrib><creatorcontrib>Murray, John D</creatorcontrib><creatorcontrib>Wang, Xiao-Jing</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pettine, Warren Woodrich</au><au>Louie, Kenway</au><au>Murray, John D</au><au>Wang, Xiao-Jing</au><au>Palminteri, Stefano</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2021-03-11</date><risdate>2021</risdate><volume>17</volume><issue>3</issue><spage>e1008791</spage><epage>e1008791</epage><pages>e1008791-e1008791</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33705386</pmid><doi>10.1371/journal.pcbi.1008791</doi><orcidid>https://orcid.org/0000-0003-3124-8474</orcidid><orcidid>https://orcid.org/0000-0003-0063-0902</orcidid><orcidid>https://orcid.org/0000-0003-4115-8181</orcidid><orcidid>https://orcid.org/0000-0002-9665-5436</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Biology and Life Sciences Brain - physiology Cereals Channel gating Computational Biology Computer and Information Sciences Conductance Decision Making Glutamic acid receptors Glutamic acid receptors (ionotropic) Grocery stores Humans Linear transformations Memory Methods Model testing Models, Neurological N-Methyl-D-aspartic acid receptors Neural networks Neural Networks, Computer Physical Sciences Population Resistance Schizophrenia Short term memory Social Sciences Time constant Uncertainty |
title | Excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice |
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