Multi-neuron Information Fusion for Direct Training Spiking Neural Networks

Spiking neural networks (SNNs) are currently receiving increasing research attention. Most existing SNNs utilize a single class of neuron models. These approaches fail to consider features such as diversity and connectivity of biological neurons, thus limiting their adaptability to different image datasets. Inspired by the gap junctions in neuroscience, we propose a multi-neuron information fusion (MIF) model. This model incorporates multiple neuron models, forming neuron groups that can reflect biological plausibility while aiming improving experimental performance. We evaluate the proposed model on the MNIST, Fashion-MNIST, CIFAR10, and N-MNIST datasets, and the experimental results show that it can achieve competitive results with fewer time steps.