Neurodynamical heterogeneous spiking neural network for spatiotemporal cognitive signal processing

Spiking neural networks (SNNs) enable low-power brain-inspired computing via sparse binary spike transmission. Nevertheless, conventional homogeneous SNNs built exclusively upon leaky integrate-and-fire (LIF) neurons exhibit limited capacity for complex multi-scale temporal cognitive dynamics. Existing parametric-heterogeneous SNNs only tune internal hyperparameters within single-compartment LIF units and cannot capture diverse neuronal computations observed in biological circuits. Drawing inspiration from cross-neuronal dynamical heterogeneity in neural systems for multi-dimensional perception and cognition, this work proposes the Neurodynamical Heterogeneous Spiking Neural Network (NHSNN). NHSNN integrates three functionally distinct spiking neuron models: LIF, dendrite-inspired Pinsky-Rinzel (PR), and leaky resonate-and-fire (LRF), with each neuron type strategically assigned to dedicated functional modules to realise cross-neuronal heterogeneous spike encoding. We evaluate NHSNN across three representative cognitive-signal modalities: clinical electroencephalogram (EEG) recordings, rhythmic auditory music data, and textual semantic sequences. On EEG-diagnosis and music-classification benchmarks, NHSNN achieves accuracy on a comparable scale relative to mainstream full-precision artificial neural networks (ANNs), though cross-study comparisons require caution owing to differing experimental protocols; on textual semantic tasks, it obtains accuracy comparable to existing SNN alternatives. Under matched network dimensionality, our approach substantially reduces parameter overhead while preserving intrinsic spike-driven energy advantages. Ablation and controlled baseline experiments verify that complementary cross-neuronal dynamical synergy - rather than raw increase of model complexity - yields strong multi-modal representations and removes requirements for heavy auxiliary modules. By balancing biological inspiration, temporal modeling capacity and computational efficiency, NHSNN provides a lightweight neuromorphic paradigm for resource-constrained edge cognitive-computing scenarios.