Spatio-Temporal Cluster-Triggered Encoding for Spiking Neural Networks
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910157223493632 |
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| author | Hu, Minchi |
| author_facet | Hu, Minchi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_08469 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spatio-Temporal Cluster-Triggered Encoding for Spiking Neural Networks Hu, Minchi Neural and Evolutionary Computing Signal Processing 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. |
| title | Spatio-Temporal Cluster-Triggered Encoding for Spiking Neural Networks |
| topic | Neural and Evolutionary Computing Signal Processing |
| url | https://arxiv.org/abs/2511.08469 |