  This paper proposes an unsupervised address event representation (AER) object
recognition approach. The proposed approach consists of a novel multiscale
spatio-temporal feature (MuST) representation of input AER events and a spiking
neural network (SNN) using spike-timing-dependent plasticity (STDP) for object
recognition with MuST. MuST extracts the features contained in both the spatial
and temporal information of AER event flow, and meanwhile forms an informative
and compact feature spike representation. We show not only how MuST exploits
spikes to convey information more effectively, but also how it benefits the
recognition using SNN. The recognition process is performed in an unsupervised
manner, which does not need to specify the desired status of every single
neuron of SNN, and thus can be flexibly applied in real-world recognition
tasks. The experiments are performed on five AER datasets including a new one
named GESTURE-DVS. Extensive experimental results show the effectiveness and
advantages of this proposed approach.
