Adaptive deep spiking neural network with global-local learning via balanced excitatory and inhibitory mechanism

The training method of Spiking Neural Networks (SNNs) is an essential problem, and how to integrate local and global learning is a worthy research interest. However, the current integration methods do not consider the network conditions suitable for local and global learning and thus fail to balance their advantages. In this paper, we propose an Excitation-Inhibition Mechanism-assisted Hybrid Learning (EIHL) algorithm that adjusts the network connectivity by using the excitationinhibition mechanism and then switches between local and global learning according to the network connectivity. The experimental results on CIFAR10/100 and DVS-CIFAR10 demonstrate that the EIHL not only obtains better accuracy performance than other methods but also has excellent sparsity advantage. Especially, the Spiking VGG11 is trained by EIHL, STBP, and STDP on DVS CIFAR10, respectively. The accuracy of the Spiking VGG11 model with EIHL is 62.45%, which is 4.35% higher than STBP and 11.40% higher than STDP. Furthermore, the sparsity achieves 18.74%, which is quite higher than the above two non-sparse methods. Moreover, the excitation-inhibition mechanism used in our method also offers a new perspective on the field of SNN learning.