# Optimal modularity and memory capacity of neural reservoirs

## TL;DR

This paper investigates how the modular structure of neural networks influences their dynamics and memory capacity, revealing an optimal modularity that maximizes memory performance by balancing local and global connectivity.

## Contribution

It demonstrates the existence of an optimal modularity in neural networks that enhances memory capacity, providing insights into neural architecture design and brain organization.

## Key findings

- Optimal modularity improves memory duration in neural networks.
- A balance between local cohesion and global connectivity is crucial.
- Insights can inform neural network design and understanding of brain structure.

## Abstract

The neural network is a powerful computing framework that has been exploited by biological evolution and by humans for solving diverse problems. Although the computational capabilities of neural networks are determined by their structure, the current understanding of the relationships between a neural network's architecture and function is still primitive. Here we reveal that neural network's modular architecture plays a vital role in determining the neural dynamics and memory performance of the network of threshold neurons. In particular, we demonstrate that there exists an optimal modularity for memory performance, where a balance between local cohesion and global connectivity is established, allowing optimally modular networks to remember longer. Our results suggest that insights from dynamical analysis of neural networks and information spreading processes can be leveraged to better design neural networks and may shed light on the brain's modular organization.