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.

翻译：对神经架构搜索的发育方法通过自组织从紧凑基因组生成功能性网络，但这些网络在生长后权重固定运行。我们表征在50,000个形态发生生长的循环控制器（CartPole和Acrobot上的500万以上配置）中的赫布和反赫布可塑性，然后测试共进化实验——其中可塑性参数编码在基因组中并与发育架构共同进化——是否独立恢复这些模式。我们的表征显示：（1）反赫布可塑性在能力网络中显著优于赫布可塑性（Cohen's d = 0.53-0.64），（2）遗憾值（最佳固定设置下丢失的预言机改进比例）达到52-100%，（3）可塑性的角色从微调转变为非平稳条件下的真正适应。共进化独立发现这些模式：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η且符号混合——完全匹配表征。随机RNN对照表明，反赫布优势对小型循环网络具有通用性，但拓扑依赖程度是发育特异的：对于形态发生生长的网络，遗憾值比具有匹配拓扑统计的随机图高2-6倍。