发表机构
Eindhoven University of Technology; University of Twente（埃因霍温理工大学; 特文特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合脉冲动力学与概率推理的贝叶斯控制算法，以山地停车问题为基准验证其在不确定环境中实时更新状态、生成目标导向动作的能力，有望成为连接计算神经科学与概率控制理论的桥梁。
AI 中文摘要
本文提出了一种贝叶斯控制框架，该框架将基于脉冲（spike-based）的动力学与概率推理相结合，用于自适应控制。贝叶斯推理被广泛认为是大脑功能的核心计算原理，为不确定性下的感知、决策和学习提供了规范框架。通过将受生物学启发的脉冲神经网络模型与贝叶斯推理原理相结合，我们提出了一种类脑控制算法，该算法能够在不确定环境中运行。我们采用具有非线性动力学的山地停车问题（mountain car parking problem）作为基准。结果表明，所提出的控制器能够实时更新状态，并通过脉冲驱动的动力学生成目标导向的行动计划。这些结果凸显了该模型作为计算神经科学与概率控制理论之间桥梁的潜力。
英文摘要
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.
CommentsAccepted at NCTA 2026 (18th Int'l Conf. on Neural Computation Theory and Applications), part of IJCCI 2026, Angers, France. Pre-peer-review submitted version
