Biological Connectivity Patterns as a Blueprint for Efficient Neural Architectures in Reinforcement Learning

Artificial neural networks (ANNs) demonstrate remarkable performance across various domains, yet their computational demands remain a significant challenge. In contrast, biological neural networks achieve exceptional efficiency through sparse structured connectivity shaped by evolution, while maintaining high performance. This study investigates whether bio-inspired connectivity patterns can improve ANN design by balancing efficiency and performance. We systematically compare the performance of Neural Circuit Architectural Priors (NCAP), a neuro- inspired reinforcement learning model, with that of Multi-Layer Perceptrons (MLPs) compressed through pruning and knowledge distillation, in a swimming task. Our findings reveal that NCAP achieves comparable performance to larger, more complex MLPs with significantly fewer parameters. We also observe that sparse, but optimally connected small MLPs often outperform larger MLPs with suboptimal connectivity, underscoring the importance of connectivity in efficient learning. This work paves the way for the development of computationally efficient models that maintain high performance by integrating biological connectivity principles into ANN architectures.