STCA: Spatio-Temporal Credit Assignment with Delayed Feedback in Deep
Spiking Neural Networks
Abstract
The temporal credit assignment problem, which
aims to discover the predictive features hidden in
distracting background streams with delayed feedback, remains a core challenge in biological and
machine learning. To address this issue, we propose a novel spatio-temporal credit assignment algorithm called STCA for training deep spiking neural networks (DSNNs). We present a new spatiotemporal error backpropagation policy by defining
a temporal based loss function, which is able to
credit the network losses to spatial and temporal
domains simultaneously. Experimental results on
MNIST dataset and a music dataset (MedleyDB)
demonstrate that STCA can achieve comparable
performance with other state-of-the-art algorithms
with simpler architectures. Furthermore, STCA
successfully discovers predictive sensory features
and shows the highest performance in the unsegmented sensory event detection tasks