Recently, Multilayer Perceptron (MLP) becomes the hotspot in the field of
computer vision tasks. Without inductive bias, MLPs perform well on feature
extraction and achieve amazing results. However, due to the simplicity of their
structures, the performance highly depends on the local features communication
machenism. To further improve the performance of MLP, we introduce information
communication mechanisms from brain-inspired neural networks. Spiking Neural
Network (SNN) is the most famous brain-inspired neural network, and achieve
great success on dealing with sparse data. Leaky Integrate and Fire (LIF)
neurons in SNNs are used to communicate between different time steps. In this
paper, we incorporate the machanism of LIF neurons into the MLP models, to
achieve better accuracy without extra FLOPs. We propose a full-precision LIF
operation to communicate between patches, including horizontal LIF and vertical
LIF in different directions. We also propose to use group LIF to extract better
local features. With LIF modules, our SNN-MLP model achieves 81.9%, 83.3% and
83.5% top-1 accuracy on ImageNet dataset with only 4.4G, 8.5G and 15.2G FLOPs,
respectively, which are state-of-the-art results as far as we know.

本文介绍了如何在多层感知器（MLP）中使用漏泄整合和火（LIF）神经元来构建以 SNN 为灵感的神经网络机制，通过改进局部特征沟通机制来提高精度，最终在 ImageNet 数据集上取得 81.9％至 83.5％的 top-1 准确度的最新成果。