# Using Connectome Features to Constrain Echo State Networks

arXiv: https://arxiv.org/abs/2206.02094 · HTML: https://mlsanity.com/paper/2206.02094/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 46 references that are in the map.
- First posted on arXiv 5 Jun 2022.

## Builds on (most cited first, 12 of 12)

- [Next Generation Reservoir Computing](https://mlsanity.com/paper/2106.07688/) (2021)
- [Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics](https://mlsanity.com/paper/2103.03174/) (2021)
- [Echo State Networks trained by Tikhonov least squares are L2(μ) approximators of ergodic dynamical systems](https://mlsanity.com/paper/2005.06967/) (2020)
- [Can a Fruit Fly Learn Word Embeddings?](https://mlsanity.com/paper/2101.06887/) (2021)
- [Efficient Optimization of Echo State Networks for Time Series Datasets](https://mlsanity.com/paper/1903.05071/) (2019)
- [On the Statistical Challenges of Echo State Networks and Some Potential Remedies](https://mlsanity.com/paper/1802.07369/) (2018)
- [A Comparative Study of Reservoir Computing for Temporal Signal Processing](https://mlsanity.com/paper/1401.2224/) (2014)
- [The Asymptotic Performance of Linear Echo State Neural Networks](https://mlsanity.com/paper/1603.07866/) (2016)
- [Sparsity in Reservoir Computing Neural Networks](https://mlsanity.com/paper/2006.02957/) (2020)
- [Dynamic Graph Echo State Networks](https://mlsanity.com/paper/2110.08565/) (2021)
- [A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System](https://mlsanity.com/paper/1806.04793/) (2018)
- [Imposing Connectome-Derived Topology on an Echo State Network](https://mlsanity.com/paper/2201.09359/) (2022)

## Built on by (most cited first, 2 of 2)

- [Multifunctionality in a Connectome-Based Reservoir Computer](https://mlsanity.com/paper/2306.01885/) (2023)
- [ElegansNet: a brief scientific report and initial experiments](https://mlsanity.com/paper/2304.13538/) (2023)

## Abstract (arXiv)

We report an improvement to the conventional Echo State Network (ESN) across three benchmark chaotic time-series prediction tasks using fruit fly connectome data alone. We also investigate the impact of key connectome-derived structural features on prediction performance -- uniquely bridging neurobiological structure and machine learning function; and find that both increasing the global average clustering coefficient and modifying the position of weights -- by permuting their synapse-synapse partners -- can lead to increased model variance and (in some cases) degraded performance. In all we consider four topological point modifications to a connectome-derived ESN reservoir (null model): namely, we alter the network sparsity, re-draw nonzero weights from a uniform distribution, permute nonzero weight positions, and increase the network global average clustering coefficient. We compare the four resulting ESN model classes -- and the null model -- with a conventional ESN by conducting time-series prediction experiments on size-variants of the Mackey-Glass 17 (MG-17), Lorenz, and Rossler chaotic time series; denoting each model's performance and variance across train-validate trials.