A Spiking Neural Network Learning Rule Integrating Multi-scale Eligibility Traces and Steady-state Adaptation

Spiking neural networks (SNNs) hold substantial promise for temporal modelling and neuromorphic computing owing to their event-driven computation and binary spike communication. Existing training methods, however, still face difficult temporal credit assignment, rigid surrogate gradients, and limited adaptive capacity. Inspired by spike-timing-dependent plasticity and meta-plasticity, we propose multi-scale eligibility traces with meta-plasticity and steady-state adaptation (MEMSA), a spatiotemporally local learning rule. MEMSA combines multi-scale eligibility traces for multi-resolution temporal dependencies, an activity-dependent meta-plastic surrogate gradient, and steady-state threshold adaptation that stabilizes firing. A binary-matrix local learning signal removes global error backpropagation. MEMSA preserves linear memory complexity O(Ln) and computational complexity O(LCn) up to constant factors. On CIFAR-100, IBM DVS Gesture, and CIFAR10-DVS, MEMSA improves over recent local rules and matches standard Backpropagation Through Time on CIFAR10-DVS within overlapping standard deviations (76.64% versus 76.40%). These results show that fully local learning can attain parity with global backpropagation on neuromorphic event-driven benchmarks.