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Bio logy has been an important source of inspiration in building adaptive autonomous robotic systems. Due to the inherent complexity of these models, most biologically-inspired robotic systems tend to be ethological without linkage to underlying neural circuit ry. Yet, neural mechanisms are crucial in modeling adaptation and learning. The work presented in this paper describes a schema and neural network multi-level modeling approach to biologically inspired autonomous robotic systems. A preydoi:10.1016/j.robot.2007.05.007 fatcat:tw6muqym35czbhno5qxjz7k3te