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• W2912257336 abstract "We study the asymptotic behaviour for asymmetric neuronal dynamics in a network of Hopfield neurons. The randomness in the network is modelled by random couplings which are centered Gaussian correlated random variables. We prove that the annealed law of the empirical measure satisfies a large deviation principle without any condition on time. We prove that the good rate function of this large deviation principle achieves its minimum value at a unique Gaussian measure which is not Markovian. This implies almost sure convergence of the empirical measure under the quenched law. We prove that the limit equations are expressed as an infinite countable set of linear non Markovian SDEs." @default.
• W2912257336 created "2019-02-21" @default.
• W2912257336 creator A5006464572 @default.
• W2912257336 creator A5028181367 @default.
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• W2912257336 date "2019-05-13" @default.
• W2912257336 modified "2023-09-25" @default.
• W2912257336 title "The mean-field limit of a network of Hopfield neurons with correlated synaptic weights" @default.
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• W2912257336 hasPublicationYear "2019" @default.
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