Learning Scalable Temporal Representations in Spiking Neural Networks Without Labels

TL;DR

This paper presents a novel training paradigm for large spiking neural networks that enables self-supervised learning without labels, using a dual-path neuron architecture and temporal alignment objectives, achieving competitive ImageNet results.

Contribution

Introduces a dual-path neuron model with surrogate gradients and temporal alignment for self-supervised training of large SNNs at scale.

Findings

Achieves 70.1% top-1 accuracy on ImageNet-1K with self-supervised SNNs.

Demonstrates effective transfer to detection and segmentation tasks.

Enables scalable unlabeled learning in high-capacity SNN architectures.

Abstract

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