On large networks of integrate-and-fire neurons with short-term synaptic plasticity

This work studies the mean-field limit of large networks of interacting stochastic leaky integrate-and-fire (LIF) neurons subject to short-term synaptic depression (STD).

The macroscopic dynamics of this system is governed by a two-dimensional, non-linear McKean-Vlasov equation that couples the evolution of the neurons' membrane potentials with a synaptic depression variable.

We investigate the long-time behavior of this limit system.

To this end, we introduce an auxiliary linearized Markov process by freezing the interaction non-linearity to a constant.

By exploiting the regeneration of the membrane potential at spike times, we are able to explicitly compute the conditional expectation of the synaptic depression variable, conditionally on the potential value, under the invariant measure of this two-dimensional linear process.

This is a crucial ingredient to study time-dependent local perturbations thereof.

As a consequence we are able to identify an analytic criterion guaranteeing the local stability of any invariant probability measure of the fully non-linear system.

This stability criterion is formulated in terms of the zeros of the Laplace transform of a specific linear response function.

Finally, we provide numerical examples demonstrating that the two-dimensional framework induces a richer spectrum of long-time dynamics than purely one-dimensional models.

For example, synaptic depression can lead to low-frequency oscillations around a unique, unstable invariant measure where the oscillations are much slower than the neurons' firing rates.