# Optimal modularity and memory capacity of neural reservoirs

arXiv: https://arxiv.org/abs/1706.06511 · HTML: https://mlsanity.com/paper/1706.06511/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2017.

## Built on by (most cited first, 8 of 8)

- [Biological neurons act as generalization filters in reservoir computing](https://mlsanity.com/paper/2210.02913/) (2022)
- [Exploiting Multiple Timescales in Hierarchical Echo State Networks](https://mlsanity.com/paper/2101.04223/) (2021)
- [Hierarchical Architectures in Reservoir Computing Systems](https://mlsanity.com/paper/2105.06923/) (2021)
- [Mitigating Privacy Risk via Forget Set-Free Unlearning](https://mlsanity.com/paper/2604.10636/) (2026)
- [A Versatile Hub Model For Efficient Information Propagation And Feature Selection](https://mlsanity.com/paper/2307.02398/) (2023)
- [Boosting Reservoir Computing with Brain-inspired Adaptive Dynamics](https://mlsanity.com/paper/2504.12480/) (2025)
- [Association-sensory spatiotemporal hierarchy and functional gradient-regularised recurrent neural network with implications for schizophrenia](https://mlsanity.com/paper/2511.02722/) (2025)
- [Distilling a Modular Reservoir Through a Genomic Bottleneck](https://mlsanity.com/paper/2606.28380/) (2026)

## Abstract (arXiv)

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.