Linking structure and activity in nonlinear spiking networks.
Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC5507396?pdf=render |
| _version_ | 1819114374092029952 |
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| author | Gabriel Koch Ocker Krešimir Josić Eric Shea-Brown Michael A Buice |
| author_facet | Gabriel Koch Ocker Krešimir Josić Eric Shea-Brown Michael A Buice |
| author_sort | Gabriel Koch Ocker |
| collection | DOAJ |
| description | Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function. |
| first_indexed | 2024-12-22T04:44:17Z |
| format | Article |
| id | doaj.art-19db3c6fb2814dc4812b0bc7c728496f |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-22T04:44:17Z |
| publishDate | 2017-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-19db3c6fb2814dc4812b0bc7c728496f2022-12-21T18:38:39ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-06-01136e100558310.1371/journal.pcbi.1005583Linking structure and activity in nonlinear spiking networks.Gabriel Koch OckerKrešimir JosićEric Shea-BrownMichael A BuiceRecent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.http://europepmc.org/articles/PMC5507396?pdf=render |
| spellingShingle | Gabriel Koch Ocker Krešimir Josić Eric Shea-Brown Michael A Buice Linking structure and activity in nonlinear spiking networks. PLoS Computational Biology |
| title | Linking structure and activity in nonlinear spiking networks. |
| title_full | Linking structure and activity in nonlinear spiking networks. |
| title_fullStr | Linking structure and activity in nonlinear spiking networks. |
| title_full_unstemmed | Linking structure and activity in nonlinear spiking networks. |
| title_short | Linking structure and activity in nonlinear spiking networks. |
| title_sort | linking structure and activity in nonlinear spiking networks |
| url | http://europepmc.org/articles/PMC5507396?pdf=render |
| work_keys_str_mv | AT gabrielkochocker linkingstructureandactivityinnonlinearspikingnetworks AT kresimirjosic linkingstructureandactivityinnonlinearspikingnetworks AT ericsheabrown linkingstructureandactivityinnonlinearspikingnetworks AT michaelabuice linkingstructureandactivityinnonlinearspikingnetworks |