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This work continues the analysis of large deviations for randomly connectedneural networks models of the brain. The originality of the model relies on thefact that the directed impact of one particle onto another depends on the stateof both particles, and (ii) have random Gaussian amplitude with mean andvariance scaling as the inverse of the network size. Similarly to the spatiallyextended case, we show that under sufficient regularity assumptions, theempirical measure satisfies a large-deviation principle with good rate functionachieving its minimum at a unique probability measure, implying in particularits convergence in both averaged and quenched cases, as well as a propagationof chaos property (in the averaged case only). The class of model we considernotably includes a stochastic version of Kuramoto model with randomconnections.

Large deviations for randomly connected neural networks: II. State-dependent interactions