  We propose a new learning algorithm to train spiking neural networks (SNN)
using conventional artificial neural networks (ANN) as proxy. We couple two SNN
and ANN networks, respectively, made of integrate-and-fire (IF) and ReLU
neurons with the same network architectures and shared synaptic weights. The
forward passes of the two networks are totally independent. By assuming IF
neuron with rate-coding as an approximation of ReLU, we backpropagate the error
of the SNN in the proxy ANN to update the shared weights, simply by replacing
the ANN final output with that of the SNN. We applied the proposed proxy
learning to deep convolutional SNNs and evaluated it on two benchmarked
datasets of Fashion-MNIST and Cifar10 with 94.56% and 93.11% classification
accuracy, respectively. The proposed networks could outperform other deep SNNs
trained with tandem learning, surrogate gradient learning, or converted from
deep ANNs. Converted SNNs require long simulation times to reach reasonable
accuracies while our proxy learning leads to efficient SNNs with much smaller
simulation times. The source codes of the proposed method are publicly
available at https://github.com/SRKH/ProxyLearning.
