  Spiking neural networks (SNNs) transmit information through discrete spikes,
which performs well in processing spatial-temporal information. Due to the
non-differentiable characteristic, there still exist difficulties in designing
well-performed SNNs. Recently, SNNs trained with backpropagation have shown
superior performance due to the proposal of the gradient approximation.
However, the performance on complex tasks is still far away from the deep
neural networks. Taking inspiration from the autapse in the brain which
connects the spiking neurons with a self-feedback connection, we apply an
adaptive time-delayed self-feedback on the membrane potential to regulate the
spike precisions. As well as, we apply the balanced excitatory and inhibitory
neurons mechanism to control the spiking neurons' output dynamically. With the
combination of the two mechanisms, we propose a deep spiking neural network
with adaptive self-feedback and balanced excitatory and inhibitory neurons
(BackEISNN). The experimental results on several standard datasets have shown
that the two modules not only accelerate the convergence of the network but
also improve the accuracy. For the MNIST, FashionMNIST, and N-MNIST datasets,
our model has achieved state-of-the-art performance. For the CIFAR10 dataset,
our BackEISNN also gets remarkable performance on a relatively light structure
that competes against state-of-the-art SNNs.
