Multi-Rate Dendritic Memory Spiking Neural Networks for Multi-Timescale Neural Signal Decoding

Invasive neural recordings capture signals with distinct timescales, including action potentials, local field potentials, and dopamine concentrations. Decoding behavior from these multimodal streams requires combining signals with different timescales, sampling rates, and temporal information densities while preserving modality-specific discriminative information. Existing methods often struggle to explicitly and independently control the temporal resolution at which history is updated and the span of history that is retained, making this integration difficult. Inspired by the proximal-to-distal functional gradient of biological dendrites, we introduce MRD-SNN, a spiking network in which each neuron maintains a direct somatic pathway and multiple sparse dendritic memory branches. Each branch refreshes at its own rate and uses a Legendre representation to summarize history over a specified nominal physical horizon. We evaluate the model on three cross-modal neural decoding tasks. MRD-SNN achieves the highest accuracy on all three tasks. Ablations on multimodal neural recordings and a spiking speech benchmark show that temporal resolution and memory horizon jointly determine what a branch can capture: the update interval controls the temporal granularity of input aggregation, while the memory parameter controls the nominal span of represented history. In multimodal neural decoding, branches need sufficient coverage to capture discriminative trends spanning several seconds. In tasks that depend on local timing patterns, finer resolution preserves transient structure. Branch-combination ablations further show that, when a task requires both fine temporal detail and longer context, multiple update rates can provide complementary information and outperform the tested single-scale configurations.