Incorporate Self-Prediction to Enhance Dynamics of Spiking Neurons

Spiking Neural Networks (SNNs) offer the potential for energy-efficient computation through event-driven, sparse processing, but their training is challenged by spike non-differentiability and trade-offs among performance, efficiency, and biological plausibility. Predictive coding motivates exploring how a neuron's own input–output history can modulate its subsequent dynamics. Inspired by this, we propose a self-prediction enhanced spiking neuron framework that generates an internal prediction current from its input–output history to modulate membrane potential, with separate coefficients for input memory and spike feedback. This design introduces additional gradient pathways through the auxiliary state and provides a local feedback mechanism inspired by dendritic modulation and neuronal self-feedback. Experiments with the reference feedback rules show performance gains in most evaluated configurations across diverse architectures, neuron types, time steps, and tasks. LIF ablations show that learning the spike-feedback coefficient further improves accuracy in two architectures.