Enabling Spike-based Backpropagation in State-of-the-art Deep Neural Network Architectures.

Spiking Neural Networks (SNNs) has recently emerged as a prominent neural computing paradigm. However, the typical shallow spiking network architectures have limited capacity for expressing complex representations, while training a very deep spiking network have not been successful so far. Diverse methods have been proposed to get around this issue such as converting off-line trained deep Artificial Neural Networks (ANNs) to SNNs. However, ANN-to-SNN conversion scheme fails to capture the temporal dynamics of a spiking system. On the other hand, it is still a difficult problem to directly train deep SNNs using input spike events due to the discontinuous and non-differentiable nature of the spike signals. To overcome this problem, we propose using differentiable (but approximate) activation for Leaky Integrate-and-Fire (LIF) spiking neurons to train deep convolutional SNNs with input spike events using spike-based backpropagation algorithm. Our experiments show the effectiveness of the proposed spike-based learning strategy on state-of-the-art deep networks (VGG and Residual architectures) by achieving the best classification accuracies in MNIST, SVHN and CIFAR-10 datasets compared to other SNNs trained with spike-based learning. Moreover, we analyze sparse event-driven computations to demonstrate the efficacy of proposed SNN training method for inference operation in the spiking domain.