A Neurodynamic-Diversity-Based Spiking Network Model for Text Classification

Language models have been undergoing rapid growth and remarkable success, while requiring massive computing resource. The brain-inspired spiking neural networks (SNNs), with advantages of better biological interpretability and less energy consumption, provides a likely alternative to process language tasks in a more sustainable way. However, there are still major difficulties in representing and processing text information with SNN-based models. Comprehensively exploring the neurodynamic diversity, we propose a spiking neural network model that could taking respective advantages from both integrator and resonator neurons to address text classification tasks. With collaborations of these two dynamically different spiking neurons, our network model outperforms previous SNN-based text classification model in an all-round way with much less time steps, and even shows some extent of potential to reach the performance of a topologically equivalent artificial neural network.