Bio-Inspired Optimization Algorithms Enhance Computational Performance of Biological Neural Networks
Researchers applied four bio-inspired optimization algorithms to neural connectome-based reservoir computing networks across six species, finding that the Whale Optimization Algorithm (WOA) achieved the largest performance gains, including a 17-fold increase in memory capacity for C. elegans. The study tested networks derived from real biological connectomes — ranging from the 279-neuron worm C. elegans to human MRI-derived brain connectivity — on tasks including chaotic time-series prediction and system identification. The findings suggest that biological synaptic weights serve as a critical starting point that topology alone cannot replicate, pointing toward a new strategy for neuromorphic and reservoir computing design.
A preprint posted to arXiv presents a systematic study in which four gradient-free, bio-inspired optimizers — Particle Swarm Optimisation, Differential Evolution, Grey Wolf Optimiser, and Whale Optimisation Algorithm (WOA) — were applied to the edge weights of echo-state networks built from real biological connectomes spanning six species: C. elegans, Drosophila, mouse, rat, macaque, and human. The networks were benchmarked on four standard reservoir computing tasks: Memory Capacity, Lorenz attractor prediction, NARMA-10 system identification, and Mackey-Glass chaotic time-series prediction. WOA consistently outperformed the other three optimizers, achieving up to a 17-fold improvement in memory capacity for C. elegans (from 1.39 to 23.91) and up to an 89% reduction in normalized root mean square error on the Mackey-Glass task for the human connectome, corresponding to an average 214% improvement across all species and tasks. A key finding is that initializing optimization from biological weights consistently outperformed random initialization on the same network topology, establishing that evolved synaptic weight distributions carry computational information beyond what structural connectivity alone provides. The authors argue this positions biologically-initialized, bio-inspired optimization as a broadly effective and principled approach to connectome-based reservoir computing.
What's missing
As a preprint, this work has not yet undergone peer review. The study does not address whether the optimized weight matrices remain biologically plausible or interpretable after optimization, nor whether the performance gains generalize to real-time or hardware neuromorphic implementations. The choice of benchmark tasks, while canonical in reservoir computing, may not capture the full range of computations relevant to biological neural systems. It is also unclear how sensitive the results are to the specific parcellation schemes used for the larger connectomes (e.g., human MRI with 83 parcels).
What different sources said
- arXiv cs.AICenter
The Whale That Outswam Evolution: Swarm Intelligence Maximises Memory in Connectome Reservoirs
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