Sun, P., Zhu, L., & Botteldooren, D. (2022). Axonal Delay as a Short-Term Memory for Feed Forward Deep Spiking Neural Networks. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp43922.2022.9747411
Abstract:
The information of spiking neural networks (SNNs) are
propagated between the adjacent biological neuron by spikes,
which provides a computing paradigm with the promise of
simulating the human brain. Recent studies have found that
the time delay of neurons plays an important role in the
learning process. Therefore, configuring the precise timing
of the spike is a promising direction for understanding and
improving the transmission process of temporal information
in SNNs. However, most of the existing learning methods for
spiking neurons are focusing on the adjustment of synaptic
weight, while very few research has been working on axonal
delay. In this paper, we verify the effectiveness of integrating
time delay into supervised learning and propose a module
that modulates the axonal delay through short-term memory.
To this end, a rectified axonal delay (RAD) module is inte-
grated with the spiking model to align the spike timing and
thus improve the characterization learning ability of tempo-
ral features. Experiments on three neuromorphic benchmark
datasets : NMNIST, DVS Gesture and N-TIDIGITS18 show
that the proposed method achieves the state-of-the-art perfor-
mance while using the fewest parameters.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - RIE2020 - Advanced Manufacturing and Engineering
Grant Reference no. : A1687b0033