A decision-making Fokker–Planck model in computational neuroscience

In computational neuroscience, decision-making may be explained analyzing models based on the evolution of the average firing rates of two interacting neuron populations, e.g., in bistable visual perception problems. These models typically lead to a multi-stable scenario for the concerned dynamical...

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Bibliographic Details
Published in:Journal of mathematical biology 2011-11, Vol.63 (5), p.801-830
Main Authors: Carrillo, José Antonio, Cordier, Stéphane, Mancini, Simona
Format: Article
Language:English
Subjects:
Analysis of PDEs
Applications of Mathematics
Computational neuroscience
Convergence
Decision Making
Evolution
Firing rate
Humans
Mathematical and Computational Biology
Mathematical models
Mathematics
Mathematics and Statistics
Models, Neurological
Models, Statistical
Neurons
Neurosciences - methods
Numerical Analysis, Computer-Assisted
Stochasticity
Visual perception
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Summary:In computational neuroscience, decision-making may be explained analyzing models based on the evolution of the average firing rates of two interacting neuron populations, e.g., in bistable visual perception problems. These models typically lead to a multi-stable scenario for the concerned dynamical systems. Nevertheless, noise is an important feature of the model taking into account both the finite-size effects and the decision’s robustness. These stochastic dynamical systems can be analyzed by studying carefully their associated Fokker–Planck partial differential equation. In particular, in the Fokker–Planck setting, we analytically discuss the asymptotic behavior for large times towards a unique probability distribution, and we propose a numerical scheme capturing this convergence. These simulations are used to validate deterministic moment methods recently applied to the stochastic differential system. Further, proving the existence, positivity and uniqueness of the probability density solution for the stationary equation, as well as for the time evolving problem, we show that this stabilization does happen. Finally, we discuss the convergence of the solution for large times to the stationary state. Our approach leads to a more detailed analytical and numerical study of decision-making models applied in computational neuroscience.
ISSN:0303-6812
1432-1416
DOI:10.1007/s00285-010-0391-3