AUTHOR=Xing Yannan , Di Caterina Gaetano , Soraghan John 

TITLE=A New Spiking Convolutional Recurrent Neural Network (SCRNN) With Applications to Event-Based Hand Gesture Recognition

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 14 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.590164

DOI=10.3389/fnins.2020.590164

ISSN=1662-453X

ABSTRACT=The combination of neuromorphic visual sensors and spiking neural network offer a high efficient
bio-inspired solution to real-world applications. However, processing event- based sequences
still remains challenging because of the nature of their asynchronism and sparsity behaviour.
In this paper, a novel spiking convolutional recurrent neural network (SCRNN) architecture that
takes advantage of both convolution operation and recurrent connectivity to maintain the spatial
and temporal relations from event-based sequence data is presented. The use of recurrent
architecture enables the network to have arbitrary length of sampling window allowing the network
to exploit temporal correlations between event collections. Rather than standard ANN to SNN
conversion techniques, the network utilizes supervised Spike Layer Error Reassignment(SLAYER)
training mechanism that allows the network to directly adapt to neuromorphic(event-based) data.
The network structure is validated on the DVS gesture dataset and it has achieved a 10 class
gesture recognition accuracy of 96.59% and 11 class gesture recognition accuracy of 92.01%.