用脉冲神经网络实现脑启发式实时控制，解决不确定环境下的决策问题。
Spike-based Belief Propagation in Nonlinear Dynamical Systems
- 将脉冲神经动力学与贝叶斯推断结合，构建类脑控制算法
- 在非线性山地小车停车任务中实现实时状态更新与目标导向动作规划
- 为计算神经科学与概率控制理论提供跨领域桥梁，适合脑启发智能研究者
本文提出一种融合脉冲神经动力学与概率推断的贝叶斯控制框架，用于适应不确定环境的自适应控制。贝叶斯推断被广泛认为是大脑功能的核心计算原则，为不确定性下的感知、决策和学习提供了规范性框架。通过将生物启发的脉冲神经模型与贝叶斯推断原理相结合，我们设计了一种类脑控制算法。以具有非线性动力学的山地小车停车问题为基准测试，结果表明，所提出的控制器能够通过脉冲驱动的动力学实现实时状态更新，并生成目标导向的动作计划。该结果凸显了该模型在连接计算神经科学与概率控制理论方面的潜力。
原文摘要 · Abstract (English)
This paper presents a Bayesian control framework that integrates spike-based dynamics with probabilistic inference for adaptive control. Bayesian inference is widely regarded as a core computational principle of brain function, providing a normative framework for perception, decision-making, and learning under uncertainty. By combining a biologically inspired spiking neural model with Bayesian inference principles, we propose a brain-like control algorithm capable of operating in uncertain environments. We use the mountain car parking problem as a benchmark with non-linear dynamics. Our results demonstrate that the proposed controller can successfully update states in real time and generate goal-directed action plans through spike-driven dynamics. The results highlight the proposed model's potential as a bridge between computational neuroscience and probabilistic control theory.
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