A Spiking Neural Network Implementation of Gaussian Belief Propagation

• URL: http://arxiv.org/abs/2512.10638v1
• Date: Thu, 11 Dec 2025 13:43:42 GMT
• Title: A Spiking Neural Network Implementation of Gaussian Belief Propagation
• Authors: Sepideh Adamiat, Wouter M. Kouw, Bert de Vries,
• Abstract summary: We investigate a distributed form of Bayesian inference, namely message passing on factor graphs.<n>We perform belief propagation by encoding messages that come into factor nodes as spike-based signals.<n>Three core linear operations, equality (branching), addition, and multiplication, are realized in networks of leaky integrate-and-fire models.
• Score: 3.05103886673788
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bayesian inference offers a principled account of information processing in natural agents. However, it remains an open question how neural mechanisms perform their abstract operations. We investigate a hypothesis where a distributed form of Bayesian inference, namely message passing on factor graphs, is performed by a simulated network of leaky-integrate-and-fire neurons. Specifically, we perform Gaussian belief propagation by encoding messages that come into factor nodes as spike-based signals, propagating these signals through a spiking neural network (SNN) and decoding the spike-based signal back to an outgoing message. Three core linear operations, equality (branching), addition, and multiplication, are realized in networks of leaky integrate-and-fire models. Validation against the standard sum-product algorithm shows accurate message updates, while applications to Kalman filtering and Bayesian linear regression demonstrate the framework's potential for both static and dynamic inference tasks. Our results provide a step toward biologically grounded, neuromorphic implementations of probabilistic reasoning.

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