Biologically Plausible Online Hebbian Meta-Learning: Two-Timescale Local Rules for Spiking Neural Brain Interfaces

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 0. 63 Zenodo, R 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.