# Multifunctionality in a Connectome-Based Reservoir Computer

arXiv: https://arxiv.org/abs/2306.01885 · HTML: https://mlsanity.com/paper/2306.01885/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Multifunctionality in a Reservoir Computer](https://mlsanity.com/paper/2008.06348/) (2020)
- [Seeing double with a multifunctional reservoir computer](https://mlsanity.com/paper/2305.05799/) (2023)
- [Exploring the limits of multifunctionality across different reservoir computers](https://mlsanity.com/paper/2205.11375/) (2022)
- [Sparsity in Reservoir Computing Neural Networks](https://mlsanity.com/paper/2006.02957/) (2020)
- [Imposing Connectome-Derived Topology on an Echo State Network](https://mlsanity.com/paper/2201.09359/) (2022)
- [Using Connectome Features to Constrain Echo State Networks](https://mlsanity.com/paper/2206.02094/) (2022)

## Built on by (most cited first, 3 of 3)

- [Exploring the origins of switching dynamics in a multifunctional reservoir computer](https://mlsanity.com/paper/2408.15400/) (2024)
- [Multifunctional physical reservoir computing in soft tensegrity robots](https://mlsanity.com/paper/2507.21496/) (2025)
- [Storage and selection of multiple chaotic attractors in minimal reservoir computers](https://mlsanity.com/paper/2603.15155/) (2026)

## Abstract (arXiv)

Multifunctionality describes the capacity for a neural network to perform multiple mutually exclusive tasks without altering its network connections; and is an emerging area of interest in the reservoir computing machine learning paradigm. Multifunctionality has been observed in the brains of humans and other animals: particularly, in the lateral horn of the fruit fly. In this work, we transplant the connectome of the fruit fly lateral horn to a reservoir computer (RC), and investigate the extent to which this 'fruit fly RC' (FFRC) exhibits multifunctionality using the 'seeing double' problem as a benchmark test. We furthermore explore the dynamics of how this FFRC achieves multifunctionality while varying the network's spectral radius. Compared to the widely-used Erd"os-Renyi Reservoir Computer (ERRC), we report that the FFRC exhibits a greater capacity for multifunctionality; is multifunctional across a broader hyperparameter range; and solves the seeing double problem far beyond the previously observed spectral radius limit, wherein the ERRC's dynamics become chaotic.