A Modular Approach to Construction of Spiking Neural Networks

In this paper, we propose a modular approach to construction of multi-layer spiking neural networks with a Coulomb energy function based learning algorithm for training each module. In this approach, a single-layer spiking neural network is constructed and trained with the Coulomb energy function ba...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Proceedings of ... International Joint Conference on Neural Networks S. 1 - 8
Hauptverfasser:	Lee, Kyunghee, Shi, Hongchi
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.07.2019
Schlagworte:	Biological neural networks Classification algorithms Coulomb energy function Delays Firing learning algorithm modular approach Neurons Potential energy SpikeProp spiking neural networks Training
ISSN:	2161-4407
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	In this paper, we propose a modular approach to construction of multi-layer spiking neural networks with a Coulomb energy function based learning algorithm for training each module. In this approach, a single-layer spiking neural network is constructed and trained with the Coulomb energy function based learning algorithm. If the learning result is not sufficiently good, another layer is added, and the input is the output of the previous layer. The process continues until a desired learning result is achieved. The approach eliminates the need for advance determination of the number of hidden layers and the need for error-backpropagation training in multi-layer spiking neural networks. Experimental results of classifying a two-ring-shaped dataset and segmenting an aerial image show that our proposed modular multi-layer spiking neural network requires a simple learning algorithm and achieves better results compared with other approaches.
ISSN:	2161-4407
DOI:	10.1109/IJCNN.2019.8851740