2026
Advancing Spatiotemporal Representations in Spiking Neural Networks via Parametric Invertible Transformation
ICLR 2026poster
Spiking Neural Networks (SNNs) are regarded as energy-efficient neural architectures due to their event-driven, spike-based computation paradigm. However, existing SNNs suffer from two fundamental limitations: (1) the constrained representational space imposed by binary spike firing mechanisms, whic…