# A system of interacting neurons with short term synaptic facilitation

## TL;DR

This paper introduces a stochastic model of neuron networks with short-term plasticity, demonstrating how such systems can temporarily retain memory of initial stimuli before relaxing to equilibrium.

## Contribution

It provides a rigorous derivation and analysis of a large population limit for a neuron model with short-term synaptic facilitation, linking microscopic dynamics to macroscopic behavior.

## Key findings

- Short-term memory corresponds to the system remaining in a specific configuration region.
- The model captures the dynamics of residual calcium and membrane potential interactions.
- The large population limit is rigorously justified and analyzed.

## Abstract

In this paper we present a simple microscopic stochastic model describing short term plasticity within a large homogeneous network of interacting neurons. Each neuron is represented by its membrane potential and by the residual calcium concentration within the cell at a given time. Neurons spike at a rate depending on their membrane potential. When spiking, the residual calcium concentration of the spiking neuron increases by one unit. Moreover, an additional amount of potential is given to all other neurons in the system. This amount depends linearly on the current residual calcium concentration within the cell of the spiking neuron. In between successive spikes, the potentials and the residual calcium concentrations of each neuron decrease at a constant rate. We show that in this framework, short time memory can be described as the tendency of the system to keep track of an initial stimulus by staying within a certain region of the space of configurations during a short but macroscopic amount of time before finally being kicked out of this region and relaxing to equilibrium. The main technical tool is a rigorous justification of the passage to a large population limit system and a thorough study of the limit equation.