  Spiking neural networks (SNNs) based on Leaky Integrate and Fire (LIF) model
have been applied to energy-efficient temporal and spatiotemporal processing
tasks. Thanks to the bio-plausible neuronal dynamics and simplicity, LIF-SNN
benefits from event-driven processing, however, usually faces the embarrassment
of reduced performance. This may because in LIF-SNN the neurons transmit
information via spikes. To address this issue, in this work, we propose a Leaky
Integrate and Analog Fire (LIAF) neuron model, so that analog values can be
transmitted among neurons, and a deep network termed as LIAF-Net is built on it
for efficient spatiotemporal processing. In the temporal domain, LIAF follows
the traditional LIF dynamics to maintain its temporal processing capability. In
the spatial domain, LIAF is able to integrate spatial information through
convolutional integration or fully-connected integration. As a spatiotemporal
layer, LIAF can also be used with traditional artificial neural network (ANN)
layers jointly. Experiment results indicate that LIAF-Net achieves comparable
performance to Gated Recurrent Unit (GRU) and Long short-term memory (LSTM) on
bAbI Question Answering (QA) tasks, and achieves state-of-the-art performance
on spatiotemporal Dynamic Vision Sensor (DVS) datasets, including MNIST-DVS,
CIFAR10-DVS and DVS128 Gesture, with much less number of synaptic weights and
computational overhead compared with traditional networks built by LSTM, GRU,
Convolutional LSTM (ConvLSTM) or 3D convolution (Conv3D). Compared with
traditional LIF-SNN, LIAF-Net also shows dramatic accuracy gain on all these
experiments. In conclusion, LIAF-Net provides a framework combining the
advantages of both ANNs and SNNs for lightweight and efficient spatiotemporal
information processing.
