Abstract
In order to elucidate higher functions of brain, it is critical to analyze changes electrical activity in a neuronal network quantitatively. We have developed the neuro-robot, which moves according to the response patterns of the neural network evoked by electrical inputs from outer world. In this study, we utilized Self-Organization-Map (SOM) to generate robot behavior without interception of teacher learning. According to IR sensor value of the robot, the neuronal network stimulated using a specific electrode, and a 64-degrees-feature vector of spatiotemporal pattern of neuronal electrical activity was generated. The values of these 64 nodes are inputted to two-dimensional output layer with 20×20 nodes of SOM. The SOM performed teacher learning only for the initial process in order to map different electrical patterns evoked by different inputs to distinct nodes. As a result, neuro-robot was succeeded in performing collision avoidance behavior.
