# Spike-based Belief Propagation in Nonlinear Dynamical Systems

arXiv: https://arxiv.org/abs/2608.19907 · HTML: https://mlsanity.com/paper/2608.19907/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 20 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [Deep Active Inference](https://mlsanity.com/paper/1709.02341/) (2017)
- [Reactive Message Passing for Scalable Bayesian Inference](https://mlsanity.com/paper/2112.13251/) (2021)
- [Brain-like Variational Inference](https://mlsanity.com/paper/2410.19315/) (2024)
- [A Spiking Neural Network Implementation of Gaussian Belief Propagation](https://mlsanity.com/paper/2512.10638/) (2025)

## Abstract (arXiv)

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.