S.M.: Embodying Cultured Networks with a Robotic Drawing Arm

Abstract — 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 robotic or simulated animal (animat) and situating them within an environment, we study how the basic principles of neuronal network communication can culminate into adaptive goal-directed behavior. We engineered a closed-loop biological-robotic drawing machine and explored sensory-motor mappings and training. Preliminary results suggest that real-time performance-based feedback allowed an animat to draw in desired directions. 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. Here, we built a robotic drawing machine named Meart (Multi-Electrode Array aRT) and designed closed-loop training algorithms in order to achieve the goal-directed behavior of drawing geometrical shapes. The algorithms were optimized using a living network connected to a simulated body, whereby the animat repeatedly learned to draw in different desired directions. Encoding more detailed sensation and motor output could produce increasingly complex and interesting behaviors. II. METHODS & PRELIMINARY RESULTS Three major topics need to be addressed when constructing embodied cultured networks: the hardware (or software) implementation of the body, the sensory-motor transformations and training algorithms, and the nurturing of the biological brain. I