Document Type : Full Research Paper

Authors

• Hossein Banki-Koshki ¹
• Seyyed Ali Seyyedsalehi ²

¹ Bioelectrics Department, Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran

² Amirkabir University of Technology Biomedical Engineering Faculty

Abstract

Neuronal synchronization as a significant cognitive phenomenon of the human brain, has attracted the interest of neuroscience researchers in recent years. This phenomenon is generally investigated in discrete and continuous neuronal models or experimentally recorded signals of the brain. In this study, for the first time, we investigate the weight synchronization instead of neuronal synchrony, in the training step of the artificial feedforward neural networks. The findings of the study show that the generalized weight synchronization occurs both during the training mode and in the non-training mode. Furthermore, as the training is completed, the synchronization increases between the weights. In this study, a new method is introduced in order to detect synchrony patterns using signal derivative and hierarchical clustering. We have also presented a criterion to quantify weight synchronization in different layers of the neural network. Accordingly, the results demonstrate that the lower layers of the network have a significantly higher level of weight synchrony than the upper layers.

Keywords

• Neuronal Synchronization
• Weight Synchronization
• Synaptic Plasticity
• Artificial Neural Network
• Neuronal Map
• Cognition
• Modeling
• Chaos

Main Subjects

• Biological Systems Modeling