A Bio-Inspired Hierarchical Spiking Neural Network With Reward-Modulated STDP Learning Rule for AER Object Recognition

The inherent spike-based and event-driven computation makes spiking neural networks (SNNs) naturally suitable to provide efficient and low-latency solution in neuromorphic vision processing. In this work we propose an address-event representation (AER) object recognition system which consists of a noise filter, an event stream segmentation module and a hierarchical convolutional SNN. In order to improve the feature learning capacity of bio-inspired shallow hierarchical SNNs for AER object recognition, we apply a convolutional SNN with R-STDP learning rule and first-spike coding to further extract features after primary feature learning using Gabor filter-based event-driven convolution. The R-STDP learning rule enables the network to adjust weights by combining local STDP learning rule with a global reward signal and to make classification without using an external classifier. Experimental results show that our method significantly outperforms the existing hierarchical SNN-based recognition methods on four popular AER datasets. Moreover, our method has much better recognition ability when using very short input event stream, and can effectively learn from small size training sets. In addition, the robustness of the network to input noise is improved. Our method is very beneficial for developing neuromorphic-vision object recognition algorithm in resource-constrained applications when event streams are short and training samples are limited.