Spatio-Temporal Cluster-Triggered Encoding for Spiking Neural Networks

2026-04-23 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Advanced, long

Summary

A novel cluster-based encoding approach, Spatio-Temporal Cluster-Triggered Encoding (ST3D), is proposed to efficiently convert static images and event streams into spike trains for Spiking Neural Networks (SNNs). This method addresses limitations of existing schemes by explicitly preserving semantic structure through local density computation in both spatial and temporal domains. The 2D spatial cluster trigger identifies foreground regions using connected component analysis and local density estimation. This is extended to a 3D spatio-temporal framework that considers temporal neighborhoods, yielding spike trains with improved consistency. Experiments on the N-MNIST dataset show that ST3D encoding achieves 98.17% classification accuracy with a simple single-layer SNN, outperforming standard Time-to-First-Spike (TTFS) encoding (97.58%) and matching complex deep architectures while using significantly fewer spikes (approximately 3800 vs. 5000 per sample).

Key takeaway

For Research Scientists developing SNNs for visual tasks, consider integrating cluster-triggered encoding methods like ST3D. This approach allows you to achieve competitive classification accuracy (98.17% on N-MNIST) with simpler, single-layer SNN architectures while significantly reducing spike counts by 24%, directly translating to energy savings on neuromorphic hardware. Your focus can shift from complex network design to optimizing encoding for better performance and efficiency.

Key insights

Cluster-based encoding preserves semantic structure in SNN spike trains, improving accuracy and sparsity.

Principles

• Semantic information clusters spatially.
• Temporal consistency improves spike train quality.

Method

The method involves binarization, connected component analysis, and local density computation to identify and encode foreground regions, extended to 3D spatio-temporal filtering for event streams.

In practice

• Use 2D-CTE for static image encoding.
• Apply 3D-CTE for event stream processing.
• Filter low-density regions to reduce noise.

Topics

• Spiking Neural Networks
• Cluster-Triggered Encoding
• Spatio-Temporal Encoding
• Neuromorphic Computing
• N-MNIST Dataset

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.