Spatio-Temporal Cluster-Triggered Encoding for Spiking Neural Networks

• URL: http://arxiv.org/abs/2511.08469v1
• Date: Wed, 12 Nov 2025 02:00:24 GMT
• Title: Spatio-Temporal Cluster-Triggered Encoding for Spiking Neural Networks
• Authors: Lingyun Ke, Minchi Hu,
• Abstract summary: Spiking Neural Networks (SNNs) process visual information to process visual information efficiently.<n>Existing encoding schemes such as rate coding, Poisson encoding, and time-to-first-spike (TTFS) often ignore spatial relationships and yield temporally inconsistent spike patterns.<n>In this article, a novel cluster-based encoding approach is proposed, which leverages local density to preserve semantic structure in both spatial and temporal domains.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Encoding static images into spike trains is a crucial step for enabling Spiking Neural Networks (SNNs) to process visual information efficiently. However, existing schemes such as rate coding, Poisson encoding, and time-to-first-spike (TTFS) often ignore spatial relationships and yield temporally inconsistent spike patterns. In this article, a novel cluster-based encoding approach is proposed, which leverages local density computation to preserve semantic structure in both spatial and temporal domains. This method introduces a 2D spatial cluster trigger that identifies foreground regions through connected component analysis and local density estimation. Then, extend to a 3D spatio-temporal (ST3D) framework that jointly considers temporal neighborhoods, producing spike trains with improved temporal consistency. Experiments on the N-MNIST dataset demonstrate that our ST3D encoder achieves 98.17% classification accuracy with a simple single-layer SNN, outperforming standard TTFS encoding (97.58%) and matching the performance of more complex deep architectures while using significantly fewer spikes (~3800 vs ~5000 per sample). The results demonstrate that this approach provides an interpretable and efficient encoding strategy for neuromorphic computing applications.

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