  The Spiking Neural Network (SNN) has attracted more and more attention
recently. It adopts binary spike signals to transmit information. Benefitting
from the information passing paradigm of SNNs, the multiplications of
activations and weights can be replaced by additions, which are more
energy-efficient. However, its "Hard Reset" mechanism for the firing activity
would ignore the difference among membrane potentials when the membrane
potential is above the firing threshold, causing information loss. Meanwhile,
quantifying the membrane potential to 0/1 spikes at the firing instants will
inevitably introduce the quantization error thus bringing about information
loss too. To address these problems, we propose to use the "Soft Reset"
mechanism for the supervised training-based SNNs, which will drive the membrane
potential to a dynamic reset potential according to its magnitude, and Membrane
Potential Rectifier (MPR) to reduce the quantization error via redistributing
the membrane potential to a range close to the spikes. Results show that the
SNNs with the "Soft Reset" mechanism and MPR outperform their vanilla
counterparts on both static and dynamic datasets.
