New Neural Network Corresponding to the Evolution Process of the Brain

Abstract

In this paper, the logic is developed assuming that all parts of the brain are composed of a combination of modules that basically have the same structure. The fundamental function is the feeding behavior searching for food while avoiding the dangers. This is most necessary function of animals in the early stages of evolution and the basis of time series data processing. The module is presented by a neural network with learning capabilities based on Hebb's law and is called the basic unit. The basic units are placed on layers and the information between the layers is bidirectional. This new neural network is an extension of the traditional neural network that evolved from pattern recognition. The biggest feature is that in the process of processing time series data, the activated part in the neural network changes according to the context structure of the data. Predicts events from the context of learned behavior and selects best way. It is important to incorporate higher levels of intelligence such as learning, imitation functions furthermore long-term memory and object symbolization. A new neural network that deals the "descriptive world" that expresses past and future events to the neural network that deals the "real world" related to the familiar events is added. The scheme of neural network's function is shown using concept of category theory