Research on Bio-inspired Self-balancing Control Based on LIF Network - Yan, Zhixin; Li, Jin; Jiang, Junbang; Luo, Shanmengdai; Huang, Lifang (International Journal of Advanced AI Applications, 2026)
Human balance is a skill gradually established through a sensory-action-feedback loop, relying on repetitive training, trial-and-error mechanisms, and the dynamic plasticity of synaptic connections. In this process, sensory signals are continuously transmitted to the central nervous system, where stable motor paths are formed through learning, enabling action reuse without complex calculations. Inspired by this mechanism, this paper proposes a balance learning method based on brain-like spiking neural networks and dopamine-modulated synaptic plasticity for self-learning control of the classic inverted pendulum system. The method connects the one-hot encoded sensory neuron group with motor neurons and utilizes a reward-driven synaptic weight update mechanism to gradually master the stable control of the inverted pendulum without the need for prior models or training data. Unlike traditional control algorithms such as PID or LQR, this approach features biological realism, strong adaptability, and self-organizing behavior, providing a new perspective on bio-inspired learning strategies for artificial intelligence in continuous control tasks.