The Role of Chaotic Neural Networks in EVSF-Network

EVSF-Network is a deep neural network that processes dynamic symbols. The dynamic symbols change their meaning depending on the situation. The network is a hybrid neural network that combines a convolutional network and a chaotic neural network. The chaotic neural network detects binding information between features from the state of all binding layers of the convolutional network. The detected binding information is reflected in the output of the convolutional network as an attention. This paper presents the core features of the EVSF-Network, namely the feature co-occurrence detection and the learning. First, the properties of dynamic symbols are presented through a discussion of the binding problem. Next, the structure of EVSF-Network and how it is learned are presented. There, a scheme is presented in which synchronization groups (unit clusters) detected from the states of fully connected layer of a convolutional neural network converted to pseudo-time series are treated as binding information. Following that, an experiment with a task based on the binding problem and its results are presented. Finally, based on the experimental results, a discussion of some issues and future challenges are presented.