Activity-Dependent Plasticity in Morphogenetically-Grown Recurrent Networks
For researchers in neural architecture search and developmental AI, this work reveals that plasticity rules can be evolved alongside network growth, but the topology-dependence of plasticity is specific to morphogenetic growth, with 2-6x higher regret compared to random graphs.
The paper characterizes Hebbian and anti-Hebbian plasticity in morphogenetically grown recurrent networks, finding that anti-Hebbian plasticity significantly outperforms Hebbian (Cohen's d = 0.53-0.64) and that co-evolution independently recovers these patterns, with 70% of CartPole runs evolving anti-Hebbian plasticity (p = 0.043).
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.