Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding
Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed di...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Integrative Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnint.2022.900715/full |
| _version_ | 1818024449207697408 |
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| author | Kai S. Gansel |
| author_facet | Kai S. Gansel |
| author_sort | Kai S. Gansel |
| collection | DOAJ |
| description | Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis. |
| first_indexed | 2024-12-10T04:00:23Z |
| format | Article |
| id | doaj.art-cfec9fb06f5941ca96d41984d2ba936e |
| institution | Directory Open Access Journal |
| issn | 1662-5145 |
| language | English |
| last_indexed | 2024-12-10T04:00:23Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Integrative Neuroscience |
| spelling | doaj.art-cfec9fb06f5941ca96d41984d2ba936e2022-12-22T02:02:59ZengFrontiers Media S.A.Frontiers in Integrative Neuroscience1662-51452022-10-011610.3389/fnint.2022.900715900715Neural synchrony in cortical networks: mechanisms and implications for neural information processing and codingKai S. GanselSynchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.https://www.frontiersin.org/articles/10.3389/fnint.2022.900715/fullsynchronycell assemblyoscillationSTDPspike patterntemporal compression |
| spellingShingle | Kai S. Gansel Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding Frontiers in Integrative Neuroscience synchrony cell assembly oscillation STDP spike pattern temporal compression |
| title | Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding |
| title_full | Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding |
| title_fullStr | Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding |
| title_full_unstemmed | Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding |
| title_short | Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding |
| title_sort | neural synchrony in cortical networks mechanisms and implications for neural information processing and coding |
| topic | synchrony cell assembly oscillation STDP spike pattern temporal compression |
| url | https://www.frontiersin.org/articles/10.3389/fnint.2022.900715/full |
| work_keys_str_mv | AT kaisgansel neuralsynchronyincorticalnetworksmechanismsandimplicationsforneuralinformationprocessingandcoding |