From Brain Theory To Autonomous Robotic Agents.pdf

From Brain Theory To Autonomous Robotic Agents Alfredo Weitzenfeld1 1Departmento Académico de Computación Instituto Tecnológico Autónomo de México (ITAM) Río Hondo #1, San Angel Tizapán, CP 01000 México DF, MEXICO alfredo@itam.mx Abstract. The study of biological systems has inspired the development of a large number of neural network architectures and robotic implementations. Through both experimentation and simulation biological systems provides a means to understand the underlying mechanisms in living organisms while inspiring the development of robotic applications. Experimentation, in the form of data gathering (ethological physiological and anatomical), provides the underlying data for simulation generating predictions to be validated by theoretical models. These models provide the understanding for the underlying neural dynamics, and serve as basis for simulation and robotic experimentation. Due to the inherent complexity of these systems, a multi-level analysis approach is required where biological, theoretic and robotic systems are studied at different levels of granularity. The work presented here overviews our existing modeling approach and describes current simulation results. 1 Introduction The study of biological systems comprises a cycle of biological experimentation, computational modeling and robotics experimentation, as depicted in Figure 1. This cycle serves as framework for the study of the underlying neural mechanisms responsible for behavior in animals and serving as inspiration in designing autonomous robotic agents. To address the underlying complexity in building biologically inspired robotic systems we have developed a multi-level analysis approach integrating across different modeling and simulation levels studied primarily with respect to four different ones: (1) autonomous robotic agents, (2) behavior, (3) neural networks, and (4) detailed neurons. 1. At the highest level, autonomous robotic agents are designed to interact with the world