Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters

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 a...

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Bibliographic Details
Published in:Chaos (Woodbury, N.Y.) N.Y.), 2018-10, Vol.28 (10), p.106318-106318
Main Authors: Cabessa, Jérémie, Villa, Alessandro E. P.
Format: Article
Language:English
Subjects:
Algorithms
Animals
Basal Ganglia - physiology
Boolean algebra
Brain
Brain - physiology
Circuits
Computer Simulation
Domains
Feedback
Ganglia
Graph theory
Graphical representations
Humans
Mathematical models
Models, Theoretical
Neural networks
Neural Networks (Computer)
Neuronal Plasticity
Neurons - physiology
Nonlinear Dynamics
Parameters
Recurrent neural networks
Software
Switching theory
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Summary: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.
ISSN:1054-1500
1089-7682
DOI:10.1063/1.5042312