Comparing SNNs and RNNs on neuromorphic vision datasets: Similarities and differences

• Published in: Neural networks Vol. 132; pp. 108 - 120
• Main Authors: He, Weihua, Wu, YuJie, Deng, Lei, Li, Guoqi, Wang, Haoyu, Tian, Yang, Ding, Wei, Wang, Wenhui, Xie, Yuan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.12.2020
• Subjects: Action Potentials - physiology; Algorithms; Databases, Factual; Humans; Long short-term memory; Machine Learning; Neural Networks, Computer; Neuromorphic dataset; Neurons - physiology; Recognition, Psychology - physiology; Recurrent neural networks; Spatiotemporal dynamics; Spiking neural networks; Vision, Ocular - physiology; Long short-term memory; Recurrent neural networks; Spatiotemporal dynamics; Neuromorphic dataset; Spiking neural networks
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2020.08.001

Neuromorphic data, recording frameless spike events, have attracted considerable attention for the spatiotemporal information components and the event-driven processing fashion. Spiking neural networks (SNNs) represent a family of event-driven models with spatiotemporal dynamics for neuromorphic computing, which are widely benchmarked on neuromorphic data. Interestingly, researchers in the machine learning community can argue that recurrent (artificial) neural networks (RNNs) also have the capability to extract spatiotemporal features although they are not event-driven. Thus, the question of “what will happen if we benchmark these two kinds of models together on neuromorphic data” comes out but remains unclear. In this work, we make a systematic study to compare SNNs and RNNs on neuromorphic data, taking the vision datasets as a case study. First, we identify the similarities and differences between SNNs and RNNs (including the vanilla RNNs and LSTM) from the modeling and learning perspectives. To improve comparability and fairness, we unify the supervised learning algorithm based on backpropagation through time (BPTT), the loss function exploiting the outputs at all timesteps, the network structure with stacked fully-connected or convolutional layers, and the hyper-parameters during training. Especially, given the mainstream loss function used in RNNs, we modify it inspired by the rate coding scheme to approach that of SNNs. Furthermore, we tune the temporal resolution of datasets to test model robustness and generalization. At last, a series of contrast experiments are conducted on two types of neuromorphic datasets: DVS-converted (N-MNIST) and DVS-captured (DVS Gesture). Extensive insights regarding recognition accuracy, feature extraction, temporal resolution and contrast, learning generalization, computational complexity and parameter volume are provided, which are beneficial for the model selection on different workloads and even for the invention of novel neural models in the future.