Adaptive Goal-Directed Behavior in Embodied Cultured Networks: Living Neuronal Networks and a Simulated Model

The advanced and robust computational power of the brain is shown by the complex behaviors it produces. By embodying living cultured neuronal networks with a simulated animal (animat) and situating them within a simulated environment, we study how the basic principles of neuronal network communication can culminate into one of these behaviors: adaptive goal-directed behavior. We engineered a closed-loop hybrid system in which a cultured network controls an animat with a specific sensory-motor mapping and training algorithm. Real-time performance-based feedback allowed both living and model embodied neural networks to learn to move the animat in a desired direction. This approach may help instruct the future design of artificial neural systems and of the algorithms to interface sensory and motor prostheses with the brain.