Bistability within a small neural circuit can arise through an appropriate strength ofexcitatory recurrent feedback. The stability of a state of neural activity, measured bythe mean dwelling time before a noise-induced transition to another state, depends on theneural firing-rate curves, the net strength of excitatory feedback, the statistics ofspike times, and increases exponentially with the number of equivalent neurons in thecircuit. Here, we show that such stability is greatly enhanced by synaptic facilitationand reduced by synaptic depression. We take into account the alteration in times ofsynaptic vesicle release, by calculating distributions of inter-release intervals of asynapse, which differ from the distribution of its incoming interspike intervals when thesynapse is dynamic. In particular, release intervals produced by a Poisson spike trainhave a coefficient of variation greater than one when synapses are probabilistic andfacilitating, whereas the coefficient of variation is less than one when synapses aredepressing. However, in spite of the increased variability in postsynaptic input producedby facilitating synapses, their dominant effect is reduced synaptic efficacy at low inputrates compared to high rates, which increases the curvature of neural input-outputfunctions, leading to wider regions of bistability in parameter space and enhancedlifetimes of memory states. Our results are based on analytic methods with approximateformulae and bolstered by simulations of both Poisson processes and of circuits of noisyspiking model neurons.
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