Using neurally inspired robots to study brain function: Principles and mechanisms

Organisms whose behaviors are guided by a nervous system are by far more sophisticated than current artificial agents. Thus, the construction of behaving devices based on principles of nervous systems may have much to offer. Our group has built series of neurobiologically inspired robots to provide a heuristic for studying brain function by embedding neurobiological principles on a physical platform capable of interacting with the real world. These neurorobots have been used to study attention, action selection, perception, operant conditioning, episodic memory, and motor control through the simulation of brain regions such as the visual cortex, the neuromodulatory system, the hippocampus, and the cerebellum. Following the brain-based model, we argue that an intelligent machine should be constrained by the following design principles: (i) it should incorporate a simulated brain with detailed neuroanatomy and neural dynamics thatc ontrols behavior and shapes memory, (ii) it should organize the unlabeled signals it receives from the environment into categories without a priori knowledge or instruction, (iii) it should have a physical instantiation, which allows for active sensing and autonomous movement in the environment, (iv) it should engage in a task that is initially constrained by minimal set of innate behaviors or reflexes, (v) it should have a means to adapt the device's behavior, called value systems, when an important environmental event occurs, and (vi) it should allow comparisons with experimental data acquired from animal nervous systems. Like the brain, these devices operate according to selectional principles through which they form categorical memory, associate categories with innate value, and adapt to the environment. Moreover, this approach may provide the groundwork for the development of intelligent machines that follow neurobiological rather than computational principles in their construction.