A complex-valued widening spiking neural network.

Abstract

Traditional spiking neural networks (SNNs) transmit only spike timing to downstream neurons, discarding rich subthreshold dynamics and limiting network capacity. To address this, we propose a Complex-valued Widening Spiking Neural Network (CWSNN), which encodes temporal information (spike time) and spatial information (subthreshold membrane potential derivative) in the real and imaginary parts of complex neurons, respectively. This design enables simultaneous processing and interaction of spatiotemporal features, effectively widening the network and increasing representational capacity without increasing depth. Experiments on seven tabular and four image datasets demonstrate that CWSNN consistently outperforms existing SNNs in accuracy, convergence speed and generalization, and also shows competitive performance on regression tasks, which remain challenging for conventional SNNs.