Abstract
Developmental approaches to neural architecture search grow functional networks from compact genomes through self-organisation, but the resulting networks operate with fixed post-growth weights. We characterise Hebbian and anti-Hebbian plasticity across 50,000 morphogenetically grown recurrent controllers (5M+ configurations on CartPole and Acrobot), then test whether co-evolutionary experiments -- where plasticity parameters are encoded in the genome and evolved alongside the developmental architecture -- recover these patterns independently. Our characterisation reveals that (1) anti-Hebbian plasticity significantly outperforms Hebbian for competent networks (Cohen's d = 0.53-0.64), (2) regret (fraction of oracle improvement lost under the best fixed setting) reaches 52-100%, and (3) plasticity's role shifts from fine-tuning to genuine adaptation under non-stationarity. Co-evolution independently discovers these patterns: on CartPole, 70% of runs evolve anti-Hebbian plasticity (p = 0.043); on Acrobot, evolution finds near-zero eta with mixed signs -- exactly matching the characterisation. A random-RNN control shows that anti-Hebbian dominance is generic to small recurrent networks, but the degree of topology-dependence is developmental-specific: regret is 2-6x higher for morphogenetically grown networks than for random graphs with matched topology statistics.
Abstract (translated)
神经架构搜索的发展方法通过自组织从紧凑基因组中生成功能网络，但生成的网络使用固定的生长后权重运行。我们在5万个形态发生生长的循环控制器（在CartPole和Acrobot上超过500万个配置）中刻画赫布与反赫布可塑性，并检验协同进化实验——其中可塑性参数被编码在基因组中并与发育架构共同进化——是否能独立复现这些模式。我们的刻画揭示：(1) 对于胜任的网络，反赫布可塑性显著优于赫布可塑性（Cohen's d = 0.53-0.64）；(2) 遗憾（在最佳固定设置下损失的神谕改进比例）达到52-100%；(3) 在非平稳条件下，可塑性的角色从微调转变为真正的适应。协同进化独立发现了这些模式：在CartPole上，70%的运行进化出反赫布可塑性（p = 0.043）；在Acrobot上，进化发现接近零的eta且符号混合——与刻画完全匹配。随机RNN对照表明，反赫布主导性对小规模循环网络具有普遍性，但拓扑依赖程度具有发育特异性：形态发生生长的网络的遗憾比拓扑统计匹配的随机图高2-6倍。
URL
https://arxiv.org/abs/2604.03386
