An Artificial Neural Network Based on the Architecture of the Cerebellum for Behavior Learning

Abstract

In the last decade, artificial intelligence (AI) pervades every aspect of our lives. However, there is a gap between AI-based machine behavior and human in natural communication. The behavior of most AI is determined as a task list generated by engineers, but to obtain high-level intelligence, AI needs the ability to cluster tasks from circumstances and learn a strategy for achieving each task. In this study, we focus on the human brain architecture that gives it the ability to self-organize and generalize sensory information. We propose an Artificial Neural Network (ANN) model based on that architecture. We describe a cerebellum-based ANN model (C-ANN) and verify its capacity to learn from the phototaxic behavior acquisition of a simple two-wheeled robot. As a result, the controller of the robot is self-organized to be simple and able to achieve positive phototaxis. This result suggests that the proposed C-ANN model has the capability of supervised learning.