Learning Scalable Temporal Representations in Spiking Neural Networks Without Labels
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908670970822656 |
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| author | Zhou, Chengwei Datta, Gourav |
| author_facet | Zhou, Chengwei Datta, Gourav |
| contents | Spiking neural networks (SNNs) exhibit temporal, sparse, and event-driven dynamics that make them appealing for efficient inference. However, extending these models to self-supervised regimes remains challenging because the discontinuities introduced by spikes break the cross-view gradient correspondences required by contrastive and consistency-driven objectives. This work introduces a training paradigm that enables large SNN architectures to be optimized without labeled data. We formulate a dual-path neuron in which a spike-generating process is paired with a differentiable surrogate branch, allowing gradients to propagate across augmented inputs while preserving a fully spiking implementation at inference. In addition, we propose temporal alignment objectives that enforce representational coherence both across spike timesteps and between augmented views. Using convolutional and transformer-style SNN backbones, we demonstrate ImageNet-scale self-supervised pretraining and strong transfer to classification, detection, and segmentation benchmarks. Our best model, a fully self-supervised Spikformer-16-512, achieves 70.1% top-1 accuracy on ImageNet-1K, demonstrating that unlabeled learning in high-capacity SNNs is feasible at modern scale |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18542 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Scalable Temporal Representations in Spiking Neural Networks Without Labels Zhou, Chengwei Datta, Gourav Emerging Technologies Spiking neural networks (SNNs) exhibit temporal, sparse, and event-driven dynamics that make them appealing for efficient inference. However, extending these models to self-supervised regimes remains challenging because the discontinuities introduced by spikes break the cross-view gradient correspondences required by contrastive and consistency-driven objectives. This work introduces a training paradigm that enables large SNN architectures to be optimized without labeled data. We formulate a dual-path neuron in which a spike-generating process is paired with a differentiable surrogate branch, allowing gradients to propagate across augmented inputs while preserving a fully spiking implementation at inference. In addition, we propose temporal alignment objectives that enforce representational coherence both across spike timesteps and between augmented views. Using convolutional and transformer-style SNN backbones, we demonstrate ImageNet-scale self-supervised pretraining and strong transfer to classification, detection, and segmentation benchmarks. Our best model, a fully self-supervised Spikformer-16-512, achieves 70.1% top-1 accuracy on ImageNet-1K, demonstrating that unlabeled learning in high-capacity SNNs is feasible at modern scale |
| title | Learning Scalable Temporal Representations in Spiking Neural Networks Without Labels |
| topic | Emerging Technologies |
| url | https://arxiv.org/abs/2511.18542 |