Title: Biologically Plausible Online Hebbian Meta‐Learning: Two‐Timescale Local Rules for Spiking Neural Brain Interfaces
Primary area: applications to neuroscience & cognitive science
Keywords: spiking neural networks, online learning, eligibility traces, meta-plasticity, surrogate gradients, biologically inspired learning, recurrent SNN, reward-modulated learning, memory efficiency, Hebbian plasticity, brain-computer interface, dual-timescale learning

Abstract: Brain-Computer Interfaces face challenges from neural signal instability and memory constraints for real-time implantable applications. We introduce an online SNN decoder using local three-factor learning rules with dual-timescale eligibility traces that avoid backpropagation through time while maintaining competitive performance. Our approach combines error-modulated Hebbian updates, fast/slow trace consolidation, and adaptive learning rate control, requiring only O(1) memory versus O(T) for BPTT methods. Evaluations on two primate datasets achieve comparable decoding accuracy (Pearson $R \geq 0.63$ Zenodo, $R \geq 0.81$ MC Maze) with 28--35\% memory reduction and faster convergence than BPTT-trained SNNs. Closed-loop simulations with synthetic neural populations demonstrate adaptation to neural disruptions and learning from scratch without offline calibration. This work enables memory-efficient, continuously adaptive neural decoding suitable for resource-constrained implantable BCI systems.

TL;DR: We propose an online spiking neural network with dual-timescale plasticity and meta-adaptive learning that achieves stable, memory-efficient sequence learning without backpropagation through time.