Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network.
Recurrent connections play an important role in cortical function, yet their exact contribution to the network computation remains unknown. The principles guiding the long-term evolution of these connections are poorly understood as well. Therefore, gaining insight into their computational role and...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2016-02-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1004698 |
| _version_ | 1819133437907304448 |
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| author | Oren Shriki Dovi Yellin |
| author_facet | Oren Shriki Dovi Yellin |
| author_sort | Oren Shriki |
| collection | DOAJ |
| description | Recurrent connections play an important role in cortical function, yet their exact contribution to the network computation remains unknown. The principles guiding the long-term evolution of these connections are poorly understood as well. Therefore, gaining insight into their computational role and into the mechanism shaping their pattern would be of great importance. To that end, we studied the learning dynamics and emergent recurrent connectivity in a sensory network model based on a first-principle information theoretic approach. As a test case, we applied this framework to a model of a hypercolumn in the visual cortex and found that the evolved connections between orientation columns have a "Mexican hat" profile, consistent with empirical data and previous modeling work. Furthermore, we found that optimal information representation is achieved when the network operates near a critical point in its dynamics. Neuronal networks working near such a phase transition are most sensitive to their inputs and are thus optimal in terms of information representation. Nevertheless, a mild change in the pattern of interactions may cause such networks to undergo a transition into a different regime of behavior in which the network activity is dominated by its internal recurrent dynamics and does not reflect the objective input. We discuss several mechanisms by which the pattern of interactions can be driven into this supercritical regime and relate them to various neurological and neuropsychiatric phenomena. |
| first_indexed | 2024-12-22T09:47:17Z |
| format | Article |
| id | doaj.art-1691e5963a0e4c0eb9d2ea860ef0ac26 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-22T09:47:17Z |
| publishDate | 2016-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-1691e5963a0e4c0eb9d2ea860ef0ac262022-12-21T18:30:30ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582016-02-01122e100469810.1371/journal.pcbi.1004698Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network.Oren ShrikiDovi YellinRecurrent connections play an important role in cortical function, yet their exact contribution to the network computation remains unknown. The principles guiding the long-term evolution of these connections are poorly understood as well. Therefore, gaining insight into their computational role and into the mechanism shaping their pattern would be of great importance. To that end, we studied the learning dynamics and emergent recurrent connectivity in a sensory network model based on a first-principle information theoretic approach. As a test case, we applied this framework to a model of a hypercolumn in the visual cortex and found that the evolved connections between orientation columns have a "Mexican hat" profile, consistent with empirical data and previous modeling work. Furthermore, we found that optimal information representation is achieved when the network operates near a critical point in its dynamics. Neuronal networks working near such a phase transition are most sensitive to their inputs and are thus optimal in terms of information representation. Nevertheless, a mild change in the pattern of interactions may cause such networks to undergo a transition into a different regime of behavior in which the network activity is dominated by its internal recurrent dynamics and does not reflect the objective input. We discuss several mechanisms by which the pattern of interactions can be driven into this supercritical regime and relate them to various neurological and neuropsychiatric phenomena.https://doi.org/10.1371/journal.pcbi.1004698 |
| spellingShingle | Oren Shriki Dovi Yellin Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network. PLoS Computational Biology |
| title | Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network. |
| title_full | Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network. |
| title_fullStr | Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network. |
| title_full_unstemmed | Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network. |
| title_short | Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network. |
| title_sort | optimal information representation and criticality in an adaptive sensory recurrent neuronal network |
| url | https://doi.org/10.1371/journal.pcbi.1004698 |
| work_keys_str_mv | AT orenshriki optimalinformationrepresentationandcriticalityinanadaptivesensoryrecurrentneuronalnetwork AT doviyellin optimalinformationrepresentationandcriticalityinanadaptivesensoryrecurrentneuronalnetwork |