Spike-Based Bayesian Control for Adaptive Robot Navigation.

Bayesian inference is a fundamental principle underlying brain function, crucial for perception, decision-making, and learning under uncertainty. This research introduces a new Bayesian control framework that merges spike-based neural dynamics, inspired by biological brains, with probabilistic inference. The goal is to create a brain-like control algorithm capable of operating effectively in unpredictable environments. The proposed controller was tested using the mountain car parking problem, a benchmark known for its non-linear dynamics. Results indicate that the model can successfully update its internal states in real-time and generate appropriate, goal-directed action plans through its spike-driven mechanisms. This work highlights a potential bridge between computational neuroscience and probabilistic control theory, offering new avenues for developing more adaptive and robust autonomous systems.