# Distributed synaptic weights in a LIF neural network and learning rules

## TL;DR

This paper investigates how the distribution of synaptic weights in a large-scale LIF neural network affects its activity and learning capabilities, highlighting the role of noise in signal memorization.

## Contribution

It analyzes the impact of synaptic weight distributions on network behavior and introduces simple learning rules that shape these distributions.

## Key findings

- Synaptic weight distribution influences network discrimination capacity.
- Learning rules can generate specific synaptic weight distributions.
- Noise acts as a selection mechanism and aids in memorization.

## Abstract

Leaky integrate-and-fire (LIF) models are mean-field limits, with a large number of neurons, used to describe neural networks. We consider inhomogeneous networks structured by a connec-tivity parameter (strengths of the synaptic weights) with the effect of processing the input current with different intensities. We first study the properties of the network activity depending on the distribution of synaptic weights and in particular its discrimination capacity. Then, we consider simple learning rules and determine the synaptic weight distribution it generates. We outline the role of noise as a selection principle and the capacity to memorized a learned signal.