Benchmarking the Connectomes of Caenorhabditis elegans within the Reservoir Computing Framework

2026-09-24

AI Summary

This study investigates whether the topology of biological connectomes inherently confers computational advantages. By mapping *Caenorhabditis elegans* connectomes—acquired across different ages and measurement modalities—onto echo state networks, this work systematically evaluates their computational performance as reservoirs on neuro-inspired tasks with minimal preprocessing. It presents the first systematic comparison between biological connectomes and random models within a reservoir computing framework. The results reveal that biological wiring does not necessarily outperform random models; rather, computational efficacy is highly dependent on the connectome source and network configuration parameters. These findings highlight the sensitivity of biological network topologies to computational configurations, offering new perspectives on understanding the algorithmic value of neural connectivity.

Abstract

The aim of this work is to examine the connectomes of Caenorhabditis elegans through a computational lens using the reservoir computing framework. Connectomes are mappings of biological neural networks; C. elegans is the first organism for which physical connectomes covering the whole nervous system have been published. The connectomes of C. elegans used in this paper have been derived at different ages of the organism and are based on three different ways of measuring inter-cellular connections. They have, with minimal preprocessing, been implemented as reservoirs in the form of echo state networks, which are recurrent neural networks. In reservoir computing, the reservoir itself is not trained, rather the output of the reservoir is passed to a comparatively small read-out module in which training takes place. Training and testing is conducted in different neuro-inspired tasks, with the aim of using these tasks as a benchmark for the connectomes. This process has been repeated with different configurations of the reservoir and equally sized but randomized null models have been used for comparison. The results show that the biological wiring and a bio-informed configuration of input and output nodes of the reservoirs do not necessarily lead to better performance. Contrarily, the randomized null models are often outperforming the original connectomes on the chosen benchmarks. At the same time it becomes clear that the results depend a lot on the configuration of the reservoir and the way the connectome has been derived from the organism. Connectomes from different ages may produce varying outcome, without a clear trend becoming visible.

Problem

• Connectome
• Caenorhabditis elegans
• Reservoir computing
• Benchmarking
• Echo state networks

Innovation

• Reservoir Computing
• Connectome
• Echo State Networks
• Caenorhabditis elegans
• Null Models