A Spiking Neural Network for Visual Causal Inference with the Hidden Markov Model

Ruihuan Ren; Pengcheng Cui; Jinping Yuan; Jiaheng Song; Yiran Li; Yutong Lu; Zixuan Huang; Jianyu Wang; Weisi Liu

doi:10.26855/acc.2025.12.009

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ArticleOpen Access http://dx.doi.org/10.26855/acc.2025.12.009

A Spiking Neural Network for Visual Causal Inference with the Hidden Markov Model

Ruihuan Ren¹, Pengcheng Cui², Jinping Yuan², Jiaheng Song², Yiran Li^2,*, Yutong Lu³, Zixuan Huang³, Jianyu Wang³, Weisi Liu^2,*

¹School of Artiﬁcial Intelligence, Yantai Institute of Technology, Yantai 264005, Shandong, China.

²School of Mathematics and Information Sciences, Yantai University, Yantai 264005, Shandong, China.

³School of Life Sciences, Yantai University, Yantai 264005, Shandong, China.

*Corresponding author: Yiran Li, Weisi Liu

This work is supported by Natural Science Foundation of Shandong Province, China (Grant Number: ZR2025QC09).

Published: December 29,2025

Abstract

Causal inference serves as a fundamental cortical function that inherently involves neural communication across various cortical areas. However, the computational principles through which neurons communicate with each other to implement causal inference remain unclear. To address this question, this paper presents a two-layer spiking neural network under the hidden Markov model (HMM) framework to investigate basic visual causal inference. A hidden cause determines whether directions of paired visual stimuli share a common source, randomly. Receiving stimuli, the network incorporates neural communication to generate random responses and employs a distance-based method to infer the hidden cause. With rewards from inference, the network updates its parameters during training. The trained network achieves acceptable performance in designed visual causal inference while reproducing key neurodynamic phenomena of sparse coding and neural variability quenching. The spiking neural network integrates neural communication into an HMM-based unified framework for sparse coding, neural variability quenching, and visual causal inference.

Keywords

Causal inference; Clustering; Spiking neural network; Hidden Markov model

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How to cite this paper

A Spiking Neural Network for Visual Causal Inference with the Hidden Markov Model

How to cite this paper: Ruihuan Ren, Pengcheng Cui, Jinping Yuan, Jiaheng Song, Yiran Li, Yutong Lu, Zixuan Huang, Jianyu Wang, Weisi Liu. (2025) A Spiking Neural Network for Visual Causal Inference with the Hidden Markov Model. Advances in Computer and Communication, 6(5), 310-316.

DOI: http://dx.doi.org/10.26855/acc.2025.12.009

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