# Synchronization and long-time memory in neural networks with inhibitory hubs and synaptic plasticity

## TL;DR

This paper explores how inhibitory hubs and synaptic plasticity influence synchronization and long-term input memory in neural networks, revealing the critical roles of network structure and inhibition levels in dynamic behavior.

## Contribution

It introduces a heterogeneous mean-field approach to analyze the interplay of inhibitory hubs and synaptic plasticity in neural network dynamics, highlighting their impact on synchronization and memory.

## Key findings

- Inhibitory hubs can induce transitions between synchronous and asynchronous states.
- A metastable regime with long input memory emerges at specific inhibitory hub fractions.
- Network structure and inhibition levels critically shape neural dynamics.

## Abstract

We investigate the dynamical role of inhibitory and highly connected nodes (hub) in synchronization and input processing of leaky-integrate-and-fire neural networks with short term synaptic plasticity. We take advantage of a heterogeneous mean-field approximation to encode the role of network structure and we tune the fraction of inhibitory neurons $f_I$ and their connectivity level to investigate the cooperation between hub features and inhibition. We show that, depending on $f_I$, highly connected inhibitory nodes strongly drive the synchronization properties of the overall network through dynamical transitions from synchronous to asynchronous regimes. Furthermore, a metastable regime with long memory of external inputs emerges for a specific fraction of hub inhibitory neurons, underlining the role of inhibition and connectivity also for input processing in neural networks.