Assessing the Role of Inhibition in Stabilizing Neocortical Networks Requires Large-Scale Perturbation of the Inhibitory Population
Neurons within cortical microcircuits are interconnected with recurrent excitatory synaptic connections that are thought to amplify signals (Douglas and Martin, 2007), form selective subnetworks (Ko et al., 2011), and aid feature discrimination. Strong inhibition (Haider et al., 2013) counterbalance...
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description | Neurons within cortical microcircuits are interconnected with recurrent excitatory synaptic connections that are thought to amplify signals (Douglas and Martin, 2007), form selective subnetworks (Ko et al., 2011), and aid feature discrimination. Strong inhibition (Haider et al., 2013) counterbalances excitation, enabling sensory features to be sharpened and represented by sparse codes (Willmore et al., 2011). This balance between excitation and inhibition makes it difficult to assess the strength, or gain, of recurrent excitatory connections within cortical networks, which is key to understanding their operational regime and the computations that they perform. Networks that combine an unstable high-gain excitatory population with stabilizing inhibitory feedback are known as inhibition-stabilized networks (ISNs) (Tsodyks et al., 1997). Theoretical studies using reduced network models predict that ISNs produce paradoxical responses to perturbation, but experimental perturbations failed to find evidence for ISNs in cortex (Atallah et al., 2012). Here, we reexamined this question by investigating how cortical network models consisting of many neurons behave after perturbations and found that results obtained from reduced network models fail to predict responses to perturbations in more realistic networks. Our models predict that a large proportion of the inhibitory network must be perturbed to reliably detect an ISN regime robustly in cortex. We propose that wide-field optogenetic suppression of inhibition under promoters targeting a large fraction of inhibitory neurons may provide a perturbation of sufficient strength to reveal the operating regime of cortex. Our results suggest that detailed computational models of optogenetic perturbations are necessary to interpret the results of experimental paradigms.
Many useful computational mechanisms proposed for cortex require local excitatory recurrence to be very strong, such that local inhibitory feedback is necessary to avoid epileptiform runaway activity (an "inhibition-stabilized network" or "ISN" regime). However, recent experimental results suggest that this regime may not exist in cortex. We simulated activity perturbations in cortical networks of increasing realism and found that, to detect ISN-like properties in cortex, large proportions of the inhibitory population must be perturbed. Current experimental methods for inhibitory perturbation are unlikely to satisfy this requirement, implying that existing |
doi_str_mv | 10.1523/JNEUROSCI.0963-17.2017 |
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Many useful computational mechanisms proposed for cortex require local excitatory recurrence to be very strong, such that local inhibitory feedback is necessary to avoid epileptiform runaway activity (an "inhibition-stabilized network" or "ISN" regime). However, recent experimental results suggest that this regime may not exist in cortex. We simulated activity perturbations in cortical networks of increasing realism and found that, to detect ISN-like properties in cortex, large proportions of the inhibitory population must be perturbed. Current experimental methods for inhibitory perturbation are unlikely to satisfy this requirement, implying that existing experimental observations are inconclusive about the computational regime of cortex. Our results suggest that new experimental designs targeting a majority of inhibitory neurons may be able to resolve this question.</description><identifier>ISSN: 0270-6474</identifier><identifier>EISSN: 1529-2401</identifier><identifier>DOI: 10.1523/JNEUROSCI.0963-17.2017</identifier><identifier>PMID: 29074575</identifier><language>eng</language><publisher>United States: Society for Neuroscience</publisher><subject>Action Potentials - physiology ; Animals ; Computational neuroscience ; Counterbalances ; Excitation ; Humans ; Inhibition ; Mathematical models ; Neocortex - physiology ; Nerve Net - physiology ; Networks ; Neural Inhibition - physiology ; Neurons ; Somatosensory cortex ; Synapses</subject><ispartof>The Journal of neuroscience, 2017-12, Vol.37 (49), p.12050-12067</ispartof><rights>Copyright © 2017 Sadeh et al.</rights><rights>Copyright Society for Neuroscience Dec 6, 2017</rights><rights>Copyright © 2017 Sadeh et al. 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c442t-c8a8b7579c1d5eff1dcf6ee0aac6e25dcc8e1ff75c92c21ebea0332d093d1f513</citedby><cites>FETCH-LOGICAL-c442t-c8a8b7579c1d5eff1dcf6ee0aac6e25dcc8e1ff75c92c21ebea0332d093d1f513</cites><orcidid>0000-0002-5480-6638 ; 0000-0003-3856-826X ; 0000-0001-8159-5461</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719979/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719979/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29074575$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sadeh, Sadra</creatorcontrib><creatorcontrib>Silver, R Angus</creatorcontrib><creatorcontrib>Mrsic-Flogel, Thomas D</creatorcontrib><creatorcontrib>Muir, Dylan Richard</creatorcontrib><title>Assessing the Role of Inhibition in Stabilizing Neocortical Networks Requires Large-Scale Perturbation of the Inhibitory Population</title><title>The Journal of neuroscience</title><addtitle>J Neurosci</addtitle><description>Neurons within cortical microcircuits are interconnected with recurrent excitatory synaptic connections that are thought to amplify signals (Douglas and Martin, 2007), form selective subnetworks (Ko et al., 2011), and aid feature discrimination. Strong inhibition (Haider et al., 2013) counterbalances excitation, enabling sensory features to be sharpened and represented by sparse codes (Willmore et al., 2011). This balance between excitation and inhibition makes it difficult to assess the strength, or gain, of recurrent excitatory connections within cortical networks, which is key to understanding their operational regime and the computations that they perform. Networks that combine an unstable high-gain excitatory population with stabilizing inhibitory feedback are known as inhibition-stabilized networks (ISNs) (Tsodyks et al., 1997). Theoretical studies using reduced network models predict that ISNs produce paradoxical responses to perturbation, but experimental perturbations failed to find evidence for ISNs in cortex (Atallah et al., 2012). Here, we reexamined this question by investigating how cortical network models consisting of many neurons behave after perturbations and found that results obtained from reduced network models fail to predict responses to perturbations in more realistic networks. Our models predict that a large proportion of the inhibitory network must be perturbed to reliably detect an ISN regime robustly in cortex. We propose that wide-field optogenetic suppression of inhibition under promoters targeting a large fraction of inhibitory neurons may provide a perturbation of sufficient strength to reveal the operating regime of cortex. Our results suggest that detailed computational models of optogenetic perturbations are necessary to interpret the results of experimental paradigms.
Many useful computational mechanisms proposed for cortex require local excitatory recurrence to be very strong, such that local inhibitory feedback is necessary to avoid epileptiform runaway activity (an "inhibition-stabilized network" or "ISN" regime). However, recent experimental results suggest that this regime may not exist in cortex. We simulated activity perturbations in cortical networks of increasing realism and found that, to detect ISN-like properties in cortex, large proportions of the inhibitory population must be perturbed. Current experimental methods for inhibitory perturbation are unlikely to satisfy this requirement, implying that existing experimental observations are inconclusive about the computational regime of cortex. Our results suggest that new experimental designs targeting a majority of inhibitory neurons may be able to resolve this question.</description><subject>Action Potentials - physiology</subject><subject>Animals</subject><subject>Computational neuroscience</subject><subject>Counterbalances</subject><subject>Excitation</subject><subject>Humans</subject><subject>Inhibition</subject><subject>Mathematical models</subject><subject>Neocortex - physiology</subject><subject>Nerve Net - physiology</subject><subject>Networks</subject><subject>Neural Inhibition - physiology</subject><subject>Neurons</subject><subject>Somatosensory cortex</subject><subject>Synapses</subject><issn>0270-6474</issn><issn>1529-2401</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkdFO2zAUhq1p0-jYXgFZ2s1uUmwnjuObSahi0KkC1I5ry3FOWrM0LrYzBLd78TlQqm1XPtL_nU8--hE6oWRKOctPv1-d3y6vV7P5lMgyz6iYMkLFGzRJqcxYQehbNCFMkKwsRHGEPoRwRwgRCXqPjpgkouCCT9DvsxAgBNuvcdwAXroOsGvxvN_Y2kbremx7vIq6tp19GqkrcMb5aI3u0hwfnP8Z8BLuB-sh4IX2a8hWKQR8Az4OvtbPluQc_Xuv84_4xu2G7jn8iN61ugvwaf8eo9tv5z9ml9ni-mI-O1tkpihYzEylq1pwIQ1tOLQtbUxbAhCtTQmMN8ZUQNtWcCOZYRRq0CTPWUNk3tCW0_wYfX3x7oZ6C42BPnrdqZ23W-0fldNW_Zv0dqPW7pfigkopZBJ82Qu8ux8gRLW1wUDX6R7cEBSVXBQlF6RK6Of_0Ds3-D6dpxiRRSGrqsoTVb5QxrsQPLSHz1Cixp7VoWc19qyoUGPPafHk71MOa6_F5n8AWIKphQ</recordid><startdate>20171206</startdate><enddate>20171206</enddate><creator>Sadeh, Sadra</creator><creator>Silver, R Angus</creator><creator>Mrsic-Flogel, Thomas D</creator><creator>Muir, Dylan Richard</creator><general>Society for Neuroscience</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>7QG</scope><scope>7QR</scope><scope>7TK</scope><scope>7U7</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-5480-6638</orcidid><orcidid>https://orcid.org/0000-0003-3856-826X</orcidid><orcidid>https://orcid.org/0000-0001-8159-5461</orcidid></search><sort><creationdate>20171206</creationdate><title>Assessing the Role of Inhibition in Stabilizing Neocortical Networks Requires Large-Scale Perturbation of the Inhibitory Population</title><author>Sadeh, Sadra ; Silver, R Angus ; Mrsic-Flogel, Thomas D ; Muir, Dylan Richard</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c442t-c8a8b7579c1d5eff1dcf6ee0aac6e25dcc8e1ff75c92c21ebea0332d093d1f513</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Action Potentials - physiology</topic><topic>Animals</topic><topic>Computational neuroscience</topic><topic>Counterbalances</topic><topic>Excitation</topic><topic>Humans</topic><topic>Inhibition</topic><topic>Mathematical models</topic><topic>Neocortex - physiology</topic><topic>Nerve Net - physiology</topic><topic>Networks</topic><topic>Neural Inhibition - physiology</topic><topic>Neurons</topic><topic>Somatosensory cortex</topic><topic>Synapses</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sadeh, Sadra</creatorcontrib><creatorcontrib>Silver, R Angus</creatorcontrib><creatorcontrib>Mrsic-Flogel, Thomas D</creatorcontrib><creatorcontrib>Muir, Dylan Richard</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>The Journal of neuroscience</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sadeh, Sadra</au><au>Silver, R Angus</au><au>Mrsic-Flogel, Thomas D</au><au>Muir, Dylan Richard</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessing the Role of Inhibition in Stabilizing Neocortical Networks Requires Large-Scale Perturbation of the Inhibitory Population</atitle><jtitle>The Journal of neuroscience</jtitle><addtitle>J Neurosci</addtitle><date>2017-12-06</date><risdate>2017</risdate><volume>37</volume><issue>49</issue><spage>12050</spage><epage>12067</epage><pages>12050-12067</pages><issn>0270-6474</issn><eissn>1529-2401</eissn><abstract>Neurons within cortical microcircuits are interconnected with recurrent excitatory synaptic connections that are thought to amplify signals (Douglas and Martin, 2007), form selective subnetworks (Ko et al., 2011), and aid feature discrimination. Strong inhibition (Haider et al., 2013) counterbalances excitation, enabling sensory features to be sharpened and represented by sparse codes (Willmore et al., 2011). This balance between excitation and inhibition makes it difficult to assess the strength, or gain, of recurrent excitatory connections within cortical networks, which is key to understanding their operational regime and the computations that they perform. Networks that combine an unstable high-gain excitatory population with stabilizing inhibitory feedback are known as inhibition-stabilized networks (ISNs) (Tsodyks et al., 1997). Theoretical studies using reduced network models predict that ISNs produce paradoxical responses to perturbation, but experimental perturbations failed to find evidence for ISNs in cortex (Atallah et al., 2012). Here, we reexamined this question by investigating how cortical network models consisting of many neurons behave after perturbations and found that results obtained from reduced network models fail to predict responses to perturbations in more realistic networks. Our models predict that a large proportion of the inhibitory network must be perturbed to reliably detect an ISN regime robustly in cortex. We propose that wide-field optogenetic suppression of inhibition under promoters targeting a large fraction of inhibitory neurons may provide a perturbation of sufficient strength to reveal the operating regime of cortex. Our results suggest that detailed computational models of optogenetic perturbations are necessary to interpret the results of experimental paradigms.
Many useful computational mechanisms proposed for cortex require local excitatory recurrence to be very strong, such that local inhibitory feedback is necessary to avoid epileptiform runaway activity (an "inhibition-stabilized network" or "ISN" regime). However, recent experimental results suggest that this regime may not exist in cortex. We simulated activity perturbations in cortical networks of increasing realism and found that, to detect ISN-like properties in cortex, large proportions of the inhibitory population must be perturbed. Current experimental methods for inhibitory perturbation are unlikely to satisfy this requirement, implying that existing experimental observations are inconclusive about the computational regime of cortex. Our results suggest that new experimental designs targeting a majority of inhibitory neurons may be able to resolve this question.</abstract><cop>United States</cop><pub>Society for Neuroscience</pub><pmid>29074575</pmid><doi>10.1523/JNEUROSCI.0963-17.2017</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0002-5480-6638</orcidid><orcidid>https://orcid.org/0000-0003-3856-826X</orcidid><orcidid>https://orcid.org/0000-0001-8159-5461</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Action Potentials - physiology Animals Computational neuroscience Counterbalances Excitation Humans Inhibition Mathematical models Neocortex - physiology Nerve Net - physiology Networks Neural Inhibition - physiology Neurons Somatosensory cortex Synapses |
title | Assessing the Role of Inhibition in Stabilizing Neocortical Networks Requires Large-Scale Perturbation of the Inhibitory Population |
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