Random Networks of Spiking Neurons: Instability in the Xenopus tadpole moto-neural pattern

Details Random Networks of Spiking Neurons: Instability in the Xenopus tadpole moto-neural pattern Random Networks of Spiking Neurons: Instability in the Xenopus tadpole moto-neural pattern

2000-03-15

arxiv.org/abs/cond-mat/0003263v1

Physics

Condensed Matter

Disordered Systems and Neural Networks

Slightly revised version of the original submission (6 Aug 1999) to Phys. Rev. Lett

Scientific paper

10.1103/PhysRevLett.85.210

A large network of integrate-and-fire (IF) neurons is studied analytically when the synaptic weights are independently randomly distributed according to a gaussian distribution with arbitrary mean. The relevant order parameters are identified, and it is shown that such network is statistically equivalent to an ensemble of independent IF neurons with each input signal given by the sum of a self-interaction deterministic term and a gaussian coloured noise. The model is able to reproduce the quasi-synchronous oscillations, and the dropout of their frequency, of the CNS neurons of the swimming Xenopus tadpole. The cause of the instability of low firing-rate self-sustained activity is identified and explained. Predictions from the model are proposed for future experiments.

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