Mean Field Analysis of Large-Scale Interacting Populations of Stochastic Conductance-Based Spiking Neurons Using the Klimontovich Method
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- 作者：Daniel Gandolfo ; Roger Rodriguez ; Henry C. Tuckwell
- 关键词：Computational neuroscience ; Conductance ; based neural models ; Fitzhugh–Nagumo model ; Stochastic differential equations ; Klimontovich method ; Mean field limits ; Neural networks
- 刊名：Journal of Statistical Physics
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：166
- 期：5
- 页码：1310-1333
- 全文大小：
- 刊物类别：Physics and Astronomy
- 刊物主题：Statistical Physics and Dynamical Systems; Theoretical, Mathematical and Computational Physics; Physical Chemistry; Quantum Physics;
- 出版者：Springer US
- ISSN：1572-9613
- 卷排序：166
文摘
We investigate the dynamics of large-scale interacting neural populations, composed of conductance based, spiking model neurons with modifiable synaptic connection strengths, which are possibly also subjected to external noisy currents. The network dynamics is controlled by a set of neural population probability distributions (PPD) which are constructed along the same lines as in the Klimontovich approach to the kinetic theory of plasmas. An exact non-closed, nonlinear, system of integro-partial differential equations is derived for the PPDs. As is customary, a closing procedure leads to a mean field limit. The equations we have obtained are of the same type as those which have been recently derived using rigorous techniques of probability theory. The numerical solutions of these so called McKean–Vlasov–Fokker–Planck equations, which are only valid in the limit of infinite size networks, actually shows that the statistical measures as obtained from PPDs are in good agreement with those obtained through direct integration of the stochastic dynamical system for large but finite size networks. Although numerical solutions have been obtained for networks of Fitzhugh–Nagumo model neurons, which are often used to approximate Hodgkin–Huxley model neurons, the theory can be readily applied to networks of general conductance-based model neurons of arbitrary dimension.