  Neuromorphic vision sensor is a new bio-inspired imaging paradigm that
reports asynchronous, continuously per-pixel brightness changes called `events'
with high temporal resolution and high dynamic range. So far, the event-based
image reconstruction methods are based on artificial neural networks (ANN) or
hand-crafted spatiotemporal smoothing techniques. In this paper, we first
implement the image reconstruction work via fully spiking neural network (SNN)
architecture. As the bio-inspired neural networks, SNNs operating with
asynchronous binary spikes distributed over time, can potentially lead to
greater computational efficiency on event-driven hardware. We propose a novel
Event-based Video reconstruction framework based on a fully Spiking Neural
Network (EVSNN), which utilizes Leaky-Integrate-and-Fire (LIF) neuron and
Membrane Potential (MP) neuron. We find that the spiking neurons have the
potential to store useful temporal information (memory) to complete such
time-dependent tasks. Furthermore, to better utilize the temporal information,
we propose a hybrid potential-assisted framework (PA-EVSNN) using the membrane
potential of spiking neuron. The proposed neuron is referred as Adaptive
Membrane Potential (AMP) neuron, which adaptively updates the membrane
potential according to the input spikes. The experimental results demonstrate
that our models achieve comparable performance to ANN-based models on IJRR,
MVSEC, and HQF datasets. The energy consumptions of EVSNN and PA-EVSNN are
19.36$\times$ and 7.75$\times$ more computationally efficient than their ANN
architectures, respectively.
