  Spiking neural networks (SNNs) are well suited for spatio-temporal learning
and implementations on energy-efficient event-driven neuromorphic processors.
However, existing SNN error backpropagation (BP) methods lack proper handling
of spiking discontinuities and suffer from low performance compared with the BP
methods for traditional artificial neural networks. In addition, a large number
of time steps are typically required to achieve decent performance, leading to
high latency and rendering spike-based computation unscalable to deep
architectures. We present a novel Temporal Spike Sequence Learning
Backpropagation (TSSL-BP) method for training deep SNNs, which breaks down
error backpropagation across two types of inter-neuron and intra-neuron
dependencies and leads to improved temporal learning precision. It captures
inter-neuron dependencies through presynaptic firing times by considering the
all-or-none characteristics of firing activities and captures intra-neuron
dependencies by handling the internal evolution of each neuronal state in time.
TSSL-BP efficiently trains deep SNNs within a much shortened temporal window of
a few steps while improving the accuracy for various image classification
datasets including CIFAR10.
