Homeostatic mechanisms play a crucial role in maintaining optimal functionality within the neural circuits of the brain. By regulating physiological and biochemical processes, these mechanisms ensure the stability of an organism's internal environment, enabling it to better adapt to external changes. Among these mechanisms, the Bienenstock, Cooper, and Munro (BCM) theory has been extensively studied as a key principle for maintaining the balance of synaptic strengths in biological systems. Despite the extensive development of spiking neural networks (SNNs) as a model for bionic neural networks, no prior work in the machine learning community has integrated biologically plausible BCM formulations into SNNs to provide homeostasis. In this study, we propose a Dynamic Weight Adaptation Mechanism (DWAM) for SNNs, inspired by the BCM theory. DWAM can be integrated into the host SNN, dynamically adjusting network weights in real time to regulate neuronal activity, providing homeostasis to the host SNN without any fine-tuning. We validated our method through dynamic obstacle avoidance and continuous control tasks under both normal and specifically designed degraded conditions. Experimental results demonstrate that DWAM not only enhances the performance of SNNs without existing homeostatic mechanisms under various degraded conditions but also further improves the performance of SNNs that already incorporate homeostatic mechanisms.

翻译：稳态机制在大脑神经回路中维持最佳功能方面发挥着至关重要的作用。通过调节生理和生化过程，这些机制确保生物体内环境的稳定性，使其能够更好地适应外部变化。在这些机制中，Bienenstock、Cooper和Munro（BCM）理论作为维持生物系统突触强度平衡的关键原理已被广泛研究。尽管脉冲神经网络（SNNs）作为仿生神经网络的模型已得到长足发展，但机器学习领域尚未有研究将生物学上合理的BCM公式整合到SNNs中以提供稳态机制。在本研究中，我们受BCM理论启发，提出了一种用于SNNs的动态权重适应机制（DWAM）。DWAM可集成到宿主SNN中，实时动态调整网络权重以调节神经元活动，无需任何微调即可为宿主SNN提供稳态。我们通过在正常条件和专门设计的退化条件下的动态避障与连续控制任务验证了该方法。实验结果表明，DWAM不仅能在各种退化条件下提升原本不具备稳态机制的SNNs的性能，还能进一步改进已具备稳态机制的SNNs的表现。