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  • 标题:Sparse bursts optimize information transmission in a multiplexed neural code
  • 本地全文:下载
  • 作者:Richard Naud ; Henning Sprekeler
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2018
  • 卷号:115
  • 期号:27
  • 页码:E6329-E6338
  • DOI:10.1073/pnas.1720995115
  • 语种:English
  • 出版社:The National Academy of Sciences of the United States of America
  • 摘要:Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing-rate output, which collapses all input streams into one. We analyze the extent to which neurons can simultaneously represent multiple input streams by using a code that distinguishes spike timing patterns at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. Neurons can also demultiplex this information, using specific connectivity patterns. The anatomy of the adult mammalian cortex suggests that these connectivity patterns are used by the nervous system to maintain sparse bursting and optimal multiplexing. Contrary to firing-rate coding, our findings indicate that the physiology and anatomy of the cortex may be interpreted as optimizing the transmission of multiple independent signals to different targets.
  • 关键词:neural coding ; cerebral cortex ; multiplexing ; dendritic computation ; short-term plasticity
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