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• W2530397777 abstract "This work continues the analysis of large deviations for randomly connected neural networks models of the brain. The originality of the model relies on the fact that the directed impact of one particle onto another depends on the state of both particles, and (ii) have random Gaussian amplitude with mean and variance scaling as the inverse of the network size. Similarly to the spatially extended case, we show that under sufficient regularity assumptions, the empirical measure satisfies a large-deviation principle with good rate function achieving its minimum at a unique probability measure, implying in particular its convergence in both averaged and quenched cases, as well as a propagation of chaos property (in the averaged case only). The class of model we consider notably includes a stochastic version of Kuramoto model with random connections." @default.
• W2530397777 created "2016-10-21" @default.
• W2530397777 creator A5000435674 @default.
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• W2530397777 date "2016-01-05" @default.
• W2530397777 modified "2023-09-27" @default.
• W2530397777 title "Large deviations for randomly connected neural networks: II. State-dependent interactions" @default.
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• W2530397777 hasPublicationYear "2016" @default.
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