Reservoir computing exploits the fixed dynamics of a recurrent network for temporal processing, requiring only a trained linear readout. Biological neural connectomes, shaped by millions of years of evolution, may encode computational structure beyond what random reservoirs provide, yet whether that structure can be further enhanced by principled optimisation remains an open question. We address it by applying four gradient-free, bio-inspired optimisers (Particle Swarm Optimisation, Differential Evolution, Grey Wolf Optimiser, and Whale Optimisation Algorithm) to the edge weights of connectome-based echo-state networks across six species spanning six orders of magnitude in neural complexity: C. elegans (279 neurons), Drosophila (49 nodes), mouse (112), rat (73), macaque (29 regions, continuous FLNe synaptic strengths), and human structural MRI connectivity (83 parcels). Each connectome is evaluated on four canonical reservoir computing benchmarks: Memory Capacity (MC), Lorenz attractor prediction, NARMA-10 system identification, and Mackey-Glass chaotic time-series prediction. All four optimisers consistently outperform unoptimised biological baselines across every task and species when initialised from biological weights. WOA achieves the largest gains on every task: up to a 17x MC improvement (C. elegans: 1.39 to 23.91) and up to 89% NRMSE reduction (Mackey-Glass, human), corresponding to an average 214% improvement across all species and tasks. Crucially, random initialisation on the same topology reliably underperforms biology, establishing biological weight values as an essential inductive bias that topology alone cannot recover. These results position bio-inspired, biologically-initialised optimisation as a principled and broadly effective strategy for connectome reservoir computing across the animal kingdom.

翻译：储层计算利用循环网络的固定动态特性进行时间序列处理，仅需训练线性读出层即可实现。经过数百万年进化塑造的生物神经连接组可能编码着超越随机储层的计算结构，但这种结构能否通过原则性优化进一步增强仍是悬而未决的问题。针对该问题，我们采用四种无梯度、仿生优化器（粒子群优化、差分进化、灰狼优化器和鲸鱼优化算法），对基于连接组的回声状态网络边权重进行优化。实验涵盖神经复杂度跨越六个数量级的六个物种：秀丽隐杆线虫（279个神经元）、果蝇（49个节点）、小鼠（112个节点）、大鼠（73个节点）、猕猴（29个区域，连续FLNe突触强度）及人类结构MRI连接组（83个脑区）。每个连接组均在四种经典储层计算基准任务上评估：记忆容量（MC）、洛伦兹吸引子预测、NARMA-10系统辨识及Mackey-Glass混沌时间序列预测。结果表明，当所有优化器以生物权重初始化时，均能在所有任务和物种上始终优于未优化的生物基线。其中鲸鱼优化算法在所有任务中取得最大增益：记忆容量提升达17倍（秀丽隐杆线虫：1.39提升至23.91），均方根误差最高降低89%（人类Mackey-Glass时间序列），对应跨物种任务平均提升214%。关键发现是，相同拓扑结构下随机初始化性能始终低于生物权重，证实生物权重值构成了拓扑结构本身无法独立恢复的关键归纳偏置。这些结果将生物启发的、基于生物初始化的优化确立为跨动物王国连接组储层计算的一种原则性且广泛有效的策略。