HYBROTS: HYBRIDS OF LIVING NEURONS AND ROBOTS FOR STUDYING NEURAL COMPUTATION

Abstract

We are developing new tools to study the computational properties of living neuronal networks. We are especially interested in the collective, emergent properties at the mesoscopic scale (Freeman 2000) of thousands of brain cells working together to learn, process information, and to control behavior. We grow dissociated monolayer mammalian cortical cultures on multi-electrode arrays. We created the electronics and software necessary for a real-time feedback loop that allows the neurons to trigger their own stimulation. A key part of this loop is a system for re-embodying the in vitro network. We use the neural activity to control either simulated animals (animats) or robots. By using networks of a few thousand neurons and glia, we have tremendous access to the cells, not feasible in vivo. This allows physical and pharmacological manipulation, and continuous imaging at the millisecond and micron scales, to determine the cell- and network-level morphological correlates of learning and memory. We also model the cultured network in software; This helps direct our experiments, which then improves the model. By combining small networks of real brain cells, computer simulations, and robotics into new hybrid neural microsystems (which we call Hybrots), we hope to determine which neural properties are essential for the kinds of collective dynamics that might be used in artificially intelligent systems.