用生物启发优化算法提升脑连接组的内存能力，效果超自然进化。
The Whale That Outswam Evolution: Swarm Intelligence Maximises Memory in Connectome Reservoirs
- 用四种群智能算法优化脑连接组的权重，提升其计算性能。
- 在多种动物脑图谱上，内存容量最高提升17倍，误差降低89%。
- 生物初始权重比随机初始化更优，说明进化赋予了重要先验知识。
残差计算利用递归网络的固定动态进行时序处理，仅需训练线性读出层。生物神经连接组历经数百万年演化，可能蕴含超越随机残差网络的计算结构，但这种结构能否通过有原则的优化进一步增强仍未知。本文采用四种无梯度、生物启发式优化器（粒子群优化、差分进化、灰狼优化器、鲸鱼优化算法），对六种跨越六个数量级神经复杂性的物种脑连接组（线虫279神经元、果蝇49节点、小鼠112、大鼠73、猴29区域连续FLNe突触强度、人类结构磁共振连接83区）的边缘权重进行优化。每个连接组在四个经典残差计算基准上评估：记忆容量（MC）、Lorenz吸引子预测、NARMA-10系统辨识、Mackey-Glass混沌时间序列预测。所有优化器在各任务与物种中均优于未优化的生物基线，且初始化于生物权重。鲸鱼优化算法（WOA）表现最佳：记忆容量最高提升17倍（线虫从1.39增至23.91），Mackey-Glass任务误差降低89%，全物种平均提升214%。关键发现：相同拓扑下随机初始化始终劣于生物初始值，证明生物权重是不可或缺的归纳偏置。结果表明，生物启发、生物初始化的优化是跨物种连接组残差计算的普适有效策略。
原文摘要 · Abstract (English)
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.
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