Spatio-Temporal Cluster-Triggered Encoding for Spiking Neural Networks

TL;DR

This paper introduces a novel spatio-temporal cluster-based encoding method for spiking neural networks that preserves semantic structure, improves accuracy, and reduces spike count compared to traditional encoding schemes.

Contribution

The authors propose a new 2D and 3D spatio-temporal clustering encoding framework that enhances interpretability and efficiency in SNNs by leveraging spatial and temporal correlations.

Findings

Achieves 98.17% accuracy on N-MNIST with a single-layer SNN

Reduces spike count to 3800 per sample from 5000

Outperforms conventional TTFS encoding in accuracy and efficiency

Abstract

Encoding static images into spike trains is a fundamental step for enabling Spiking Neural Networks (SNNs) to process visual information. However, widely used methods such as rate coding, Poisson encoding, and time-to-first-spike (TTFS) often neglect spatial correlations and produce temporally inconsistent spike patterns, limiting both efficiency and interpretability. In this work, we propose a novel cluster-based encoding framework that explicitly preserves semantic structure across both spatial and temporal domains. The method first introduces a 2D spatial clustering mechanism, which leverages connected component analysis and local density estimation to identify salient foreground regions. Building upon this, we extend the approach to a 3D spatio-temporal (ST3D) encoding scheme that incorporates temporal neighborhood information, generating spike trains with enhanced temporal coherence. Experiments on the N-MNIST dataset demonstrate that the proposed ST3D encoder achieves 98.17% classification accuracy using a simple single-layer SNN, outperforming conventional TTFS encoding (97.58%). Notably, this performance is achieved with significantly fewer spikes (3800 vs. 5000 per sample), highlighting improved efficiency without sacrificing accuracy. These results indicate that the proposed method provides an interpretable, structure-aware, and computationally efficient encoding strategy, offering strong potential for neuromorphic computing applications.