Brains display very high-level parallel computation, fault-tolerance, and adaptability, all of which are what we struggle to recreate in engineered systems. The neurocomputer (an organic computer built from living neurons) seems possible and may lead to a new generation of computing device that can operate in a brain-like manner. Cultured neuronal networks on multi-electrode arrays (MEAs) are one of the best candidates for the neurocomputer for their controllability, accessibility, flexibility, and the ability to self-organize.

I explored the possibility of the neurocomputer by studying whether we can show goal-directed learning, one of the most fascinating behavior of brains, in cultured networks. Inspired by the brain, which needs to be embodied in some way and interact with its surroundings in order to give a purpose to its activities, we have developed tools for closing the sensory-motor loop between a cultured network and a robot or an artificial animal (an animat), termed a "hybrot". In order to efficiently find an effective closed-loop design among infinite potential options, I constructed a biologically-inspired simulated network. By using this simulated network, I designed: (1) a statistic that can effectively and efficiently decode network functional plasticity, and (2) feedback stimulations and an adaptive training algorithm to encode sensory information and to direct network plasticity. By closing the sensory-motor loop with these decoding and encoding designs, we successfully demonstrated a simple adaptive goal-directed behavior: learning to move in a user-defined direction, and further showed that multiple tasks could be learned simultaneously. These results suggest that even though a cultured network lacks the 3-D structure of the brain, it still can be functionally shaped and show meaningful behavior.

To our knowledge, this is the first demonstration of goal-directed learning in embodied cultured networks. Extending from these findings, I further proposed a research plan to optimize closed-loop designs for evaluating the maximal learning capacity (or even true intelligence) of the cultured network. Knowledge gained from effective closed-loop designs provides insights about learning and memory in the nervous system, which could influence the design of neurocomputers, future artificial neural networks, and more effective neuroprosthetics.