Sparse Temporal Encoding of Visual Features for Robust Object Recognition by Spiking Neurons

Robust object recognition in spiking neural systems remains a challenging in neuromorphic computing area as it needs to solve both the effective encoding of sensory information and also its integration with downstream learning neurons. We target this problem by developing a spiking neural system consisting of sparse temporal encoding and temporal classifier. We propose a sparse temporal encoding algorithm which exploits both spatial and temporal information derived from an spike-timing-dependent plasticity-based HMAX feature extraction process. The temporal feature representation, thus, becomes more appropriate to be integrated with a temporal classifier based on spiking neurons rather than with nontemporal classifier. The algorithm has been validated on two benchmark data sets and the results show the temporal feature encoding and learning-based method achieves high recognition accuracy. The proposed model provides an efficient approach to perform feature representation and recognition in a consistent temporal learning framework, which is easily adapted to neuromorphic implementations.