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  Ultrafast population coding and axo-somatic compartmentalization

Zhang, C., Hofmann, D., Neef, A., & Wolf, F. (2022). Ultrafast population coding and axo-somatic compartmentalization. PLoS Computational Biology, 18(1): e1009775. doi:10.1371/journal.pcbi.1009775.

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Zhang, Chenfei1, Author           
Hofmann, David1, Author           
Neef, Andreas1, Author           
Wolf, Fred1, Author           
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1Research Group Theoretical Neurophysics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063289              

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 Abstract: Populations of cortical neurons respond to common input within a millisecond. Morphological features and active ion channel properties were suggested to contribute to this astonishing processing speed. Here we report an exhaustive study of ultrafast population coding for varying axon initial segment (AIS) location, soma size, and axonal current properties. In particular, we studied their impact on two experimentally observed features 1) precise action potential timing, manifested in a wide-bandwidth dynamic gain, and 2) high-frequency boost under slowly fluctuating correlated input. While the density of axonal channels and their distance from the soma had a very small impact on bandwidth, it could be moderately improved by increasing soma size. When the voltage sensitivity of axonal currents was increased we observed ultrafast coding and high-frequency boost. We conclude that these computationally relevant features are strongly dependent on axonal ion channels’ voltage sensitivity, but not their number or exact location. We point out that ion channel properties, unlike dendrite size, can undergo rapid physiological modification, suggesting that the temporal accuracy of neuronal population encoding could be dynamically regulated. Our results are in line with recent experimental findings in AIS pathologies and establish a framework to study structure-function relations in AIS molecular design.

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Language(s): eng - English
 Dates: 2022-01-18
 Publication Status: Published online
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 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1009775
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Title: PLoS Computational Biology
  Abbreviation : PLoS Comput Biol
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: 25 Volume / Issue: 18 (1) Sequence Number: e1009775 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1