Oligodendrocyte-Driven Spiking Neural Model

Abstract

The spiking neuron model (SNM) mimics the processing paradigm of synaptic and membrane potentials in the cerebral cortex. However, existing SNMs are limited by two issues. First, they lack spike diversity. Although a spiking neuron perceives temporally varying input currents, SNMs only use identical synaptic weights for regulation. Second, they are insensitive to weak spikes. The potential accumulation in SNMs is solely driven by external inputs, ignoring the internal dynamics of potential. Oligodendrocytes, a recent revelation in neuroscience, enhance neural signaling by forming bidirectional communication. This offers the potential to alleviate the aforementioned issues. In this paper, we first propose the mechanism of the oligodendrocyte-spiking neuron (Oli-N) model. Subsequently, using the Oli-N model, we develop our Oli-inspired spiking neural network (Oli-SNN), which broadens the diversity of spike representations and enhances neurons' firing precision through improved sparse coding to enhance weak spikes. Experiments show that our Oli-SNN achieves state-of-the-art performance in the classification task on both static and neuromorphic datasets.