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. The architecture is hybrid, embedding a spiking neural realization of the nonlinear state-transition factor within a numerical message-passing framework. 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 online, at each control step, 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.