Cargando…
Supervised Learning Algorithm Based on Spike Train Inner Product for Deep Spiking Neural Networks
By mimicking the hierarchical structure of human brain, deep spiking neural networks (DSNNs) can extract features from a lower level to a higher level gradually, and improve the performance for the processing of spatio-temporal information. Due to the complex hierarchical structure and implicit nonl...
Autores principales: | Lin, Xianghong, Zhang, Zhen, Zheng, Donghao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9954578/ https://www.ncbi.nlm.nih.gov/pubmed/36831711 http://dx.doi.org/10.3390/brainsci13020168 |
Ejemplares similares
-
Supervised Learning Algorithm for Multilayer Spiking Neural Networks with Long-Term Memory Spike Response Model
por: Lin, Xianghong, et al.
Publicado: (2021) -
An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks
por: Xie, Xiurui, et al.
Publicado: (2016) -
Supervised learning in spiking neural networks with FORCE training
por: Nicola, Wilten, et al.
Publicado: (2017) -
SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks
por: Zenke, Friedemann, et al.
Publicado: (2018) -
First Error-Based Supervised Learning Algorithm for Spiking Neural Networks
por: Luo, Xiaoling, et al.
Publicado: (2019)