Dual-Timescale Hebbian Accumulators for Online Spiking Neural Network Decoding in Intracortical Brain Machine Interfaces

Intracortical brain-machine interfaces require decoders that adapt continuously to neural signal instability while operating within strict memory budgets. We introduce a dual-timescale Hebbian accumulator learning rule for spiking neural networks that enables per-timestep online supervised updates with training memory constant in sequence length, avoiding backpropagation through time. The rule combines synapse-specific fast and slow eligibility traces, error-modulated three-factor updates, and integer-friendly RMS homeostasis, operating without adaptive gradient optimizers (Adam, RMSProp) or replay buffers. On two primate intracortical datasets, the method achieves Pearson correlations of $R \geq 0.81$ on MC~Maze and $R \geq 0.63$ on Zenodo~Indy, with 63--86\% measured memory reduction versus BPTT at sequence length $T = 1000$. Closed-loop simulations demonstrate online adaptation to neural disruptions and learning from scratch without offline calibration.