Evolutionary optimization of spiking neural networks (SNNs) becomes increasingly difficult as task complexity grows because they must search a combined topology--parameter space that grows super-exponentially with network size. We address this scaling challenge through a co-evolutionary ensemble framework in which a population of candidate SNNs is evolved with fitness defined by each network's marginal contribution to group performance. Grounded in cooperative game theory and difference evaluation functions from multiagent systems, this credit assignment rewards networks that consistently improve ensemble performance and penalizes redundancy, encouraging complementary specialization during evolution rather than relying on post-hoc combination of independently trained networks. We evaluate the approach on classification, regression, and control tasks under $μ$Caspian neuromorphic hardware constraints. Co-evolved ensembles achieve statistically significant improvements over both single-network evolution and post-hoc ensembles across all tasks, with the most pronounced gains in control, where standard evolution fails to discover effective policies and co-evolution enables a qualitative transition to near-optimal performance.

翻译：随着任务复杂度的增加，脉冲神经网络的进化优化变得愈发困难，因为该方法必须在联合拓扑-参数空间中进行搜索，而该空间随网络规模呈超指数增长。我们通过一个协同进化集成框架应对这一规模化挑战：在该框架中，候选脉冲神经网络种群通过每个网络对群体性能的边际贡献来定义适应度进行进化。这一方法基于合作博弈论和多智能体系统中的差分评估函数，通过信用分配机制奖励持续提升集成性能的网络，并惩罚冗余网络，从而在进化过程中鼓励互补特化，而非依赖事后组合独立训练的网络。我们在 μCaspian 神经形态硬件约束下，对分类、回归和控制任务评估了该方法。在所有任务中，协同进化集成相较于单网络进化及事后集成均取得了统计显著的性能提升，其中控制任务的改进最为显著——标准进化无法发现有效策略，而协同进化使得性能实现质的飞跃，趋近于最优表现。