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- W2897816729 abstract "Studies of Boolean recurrent neural networks are briefly introduced with an emphasis on the attractor dynamics determined by the sequence of distinct attractors observed in the limit cycles. We apply this framework to a simplified model of the basal ganglia-thalamocortical circuit where each brain area is represented by a neuronal node in a directed graph. Control parameters ranging from neuronal excitability that affects all cells to targeted local connections modified by a new adaptive plasticity rule, and the regulation of the interactive feedback affecting the external input stream of information, allow the network dynamics to switch between stable domains delimited by highly discontinuous boundaries and reach very high levels of complexity with specific configurations. The significance of this approach with regard to brain circuit studies is briefly discussed." @default.
- W2897816729 created "2018-10-26" @default.
- W2897816729 creator A5019136653 @default.
- W2897816729 creator A5073692548 @default.
- W2897816729 date "2018-10-01" @default.
- W2897816729 modified "2023-10-14" @default.
- W2897816729 title "Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters" @default.
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- W2897816729 doi "https://doi.org/10.1063/1.5042312" @default.
- W2897816729 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30384642" @default.
- W2897816729 hasPublicationYear "2018" @default.
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