Pair-Aware Self-Contrastive Learning in Spiking Neural Networks

Self-Contrastive Forward-Forward (SCFF) learning enables layer-local representation learning from unlabeled data by constructing positive and negative pairs. However, conventional activity-based goodness evaluates the firing strength of a fused pair representation without explicitly assessing the correspondence between its constituent responses. This creates a mismatch between relation-based pair construction and activity-based goodness evaluation in spiking neural networks (SNNs). To address this limitation, we propose Pair-aware SCFF, which incorporates cross-branch spike correspondence into the local goodness function. Specifically, we evaluate observed spike co-activation relative to marginal firing activity and combine the resulting relational signal with evidence from activity-supported pairs. This formulation jointly accounts for neural response strength and cross-branch correspondence while preserving layer-wise local optimization without class-label supervision or network-wide error propagation. Experiments across six static-image and two event-based datasets demonstrate consistent improvements over a matched Spiking SCFF baseline, with accuracy gains of up to 10.71 percentage points. Controlled ablations distinguish the contributions of relation-aware goodness and pair-stream propagation, while pair-level analyses reveal more structured pair relations and improved class-level semantic organization in the learned representations. These results highlight the importance of explicitly evaluating cross-branch correspondence in local self-contrastive SNN learning.