Training a digital model of a deep spiking neural network using backpropagation
Deep spiking neural networks are one of the promising eventbased sensor signal processing concepts. However, the practical application of such networks is difficult with standard deep neural network training packages. In this paper, we propose a vector-matrix description of a spike neural network th...
Main Author: | Bondarev V |
---|---|
Format: | Article |
Language: | English |
Published: |
EDP Sciences
2020-01-01
|
Series: | E3S Web of Conferences |
Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/84/e3sconf_TPACEE2020_01026.pdf |
Similar Items
-
Training Deep Spiking Neural Networks using Backpropagation
by: Jun Haeng Lee, et al.
Published: (2016-11-01) -
Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures
by: Chankyu Lee, et al.
Published: (2020-02-01) -
Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networks
by: Guobin Shen, et al.
Published: (2022-06-01) -
Dynamic layer-span connecting spiking neural networks with backpropagation training
by: Zijjian Wang, et al.
Published: (2023-10-01) -
Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks
by: Yujie Wu, et al.
Published: (2018-05-01)