Spike-based Belief Propagation in Nonlinear Dynamical Systems

• URL: http://arxiv.org/abs/2608.19907v1
• Date: Thu, 20 Aug 2026 11:20:22 GMT
• Title: Spike-based Belief Propagation in Nonlinear Dynamical Systems
• Abstract summary: We propose a brain-like control algorithm capable of operating in uncertain environments.<n>We use the mountain car parking problem as a benchmark with non-linear dynamics.<n>Results highlight the proposed model's potential as a bridge between computational neuroscience and probabilistic control theory.
• Score: 3.8108588769749407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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