Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains

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• 作者：Chris Trengove ; Markus Diesmann ; Cees van Leeuwen
• 关键词：Synfire chains ; Recurrent network dynamics ; Background synaptic noise ; Effective connectivity ; Metastability ; Combinatorial representation
• 刊名：Journal of Computational Neuroscience
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：40
• 期：1
• 页码：1-26
• 全文大小：6,402 KB
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Biomedicine
  Neurosciences
  Neurology
  Human Genetics
  Theory of Computation
  
• 出版者：Springer Netherlands
• ISSN：1573-6873

文摘

As a candidate mechanism of neural representation, large numbers of synfire chains can efficiently be embedded in a balanced recurrent cortical network model. Here we study a model in which multiple synfire chains of variable strength are randomly coupled together to form a recurrent system. The system can be implemented both as a large-scale network of integrate-and-fire neurons and as a reduced model. The latter has binary-state pools as basic units but is otherwise isomorphic to the large-scale model, and provides an efficient tool for studying its behavior. Both the large-scale system and its reduced counterpart are able to sustain ongoing endogenous activity in the form of synfire waves, the proliferation of which is regulated by negative feedback caused by collateral noise. Within this equilibrium, diverse repertoires of ongoing activity are observed, including meta-stability and multiple steady states. These states arise in concert with an effective connectivity structure (ECS). The ECS admits a family of effective connectivity graphs (ECGs), parametrized by the mean global activity level. Of these graphs, the strongly connected components and their associated out-components account to a large extent for the observed steady states of the system. These results imply a notion of dynamic effective connectivity as governing neural computation with synfire chains, and related forms of cortical circuitry with complex topologies.