Neurorobot Vitroid as a model of brain-body interaction

To mimic biological intelligence, it is critical to elucidate the network dynamics of a neural network. The dissociated culture system possesses a simple network comparing to a whole brain, thus it is suitable for exploration of spatiotemporal dynamics of electrical activity of a neuronal circuit. Cultured neuronal network has no input-output system, so it requires an artificial peripheral system to interact with outer world. We are developing the neurorobot as the model system for biological information processing with vital components and the artificial peripheral system. The behavior of the neuro-robot is determined by the response pattern of neuronal electrical activity evoked by a current stimulation from outer world. In this study, we developed a novel type of neurorobot with Self-Organization Map (SOM) for a neuronal output pattern decoder. The robot with SOM is expected to perform non-stop learning and generation of behavior simultaneously. The spatiotemporal electrical patterns evoked by the inputs according to the value of the IR sensors on the robot body are translated to 64 dimension feature vectors and inputted to the SOM. Then the 64 dimension feature vectors are mapped to a certain winner vector in the 10 × 10 output layer of SOM. Winner nodes are linked to the purposive behaviors adequate to the inputs according to outer phenomenon. Only at the beginning of the behavior, neurorobot SOM selects two winner nodes premisely assigned to the specific inputs for the obstacles near the L and R side of the robot body. We call the process as “seeding”. After the seeding process, the distribution of winner units for the two inputs were separated each other, when the spatiotemporal pattern of electrical activity were not overlapped. In addition, the position of the centers of winner nodes gravities, updated with every input, are almost stable in the output layer of the SOM.