Deep neural network for dynamic symbol

Abstract

In this paper, EVSF-Network, a deep neural network for dynamic symbols, is presented. EVSF-Network is an extension of VSF-Network, which was developed for the same purpose. The symbols that can be processed by VSF-Network are static symbols, while those that can be processed by EVSF-Network are dynamic symbols. Unlike static symbols, dynamic symbols have the property of changing their meaning according to the situation. It is a hybrid neural network that combines a hierarchical neural network and a chaotic neural network. The hierarchical neural networks perform feature extraction. The chaotic neural network extracts co-occurrence relations between perceptual features depending on situations from the internal state of the hierarchical neural network. The co-occurrence relations between features found by the chaotic neural network are reflected in the output of the hierarchical neural network by attention. First, the binding problem is discussed as a problem related to basic properties about dynamic symbols. A simple example of the binding problem is introduced, followed by an explanation of the synchronization group of neurons, a neuroscience model of the binding problem. EVSF-Network expresses situation dependency in dynamic symbols by means of synchronization groups. Next, the structure of EVSF -Network and its learning procedure are presented. Following that, an experiment using an image recognition task dealing with the binding problem by Rosenblatt et al. and their results are presented. Finally, a discussion about future extensions to the EVSF-Network is shown.