Silvia Ferrari, Craig Henriquez, and Antonius M.J. VanDongen
Duke University
The goal of the proposed activity is to develop a methodology for training cultured neuronal networks to solve challenging problems in optimal control and prediction. Engineering systems, such as aerospace and robotic systems, already benefit from feedback controllers and estimators that are man-designed to handle normal operating conditions for which system dynamics are known a priori. However, these designs are not yet capable of handling unforeseen damages and failures involving highly nonlinear and unmodeled dynamics that are unknown a priori. Biological systems are capable of operating optimally, subject to a variety of constraints, and to learn and adapt in real time when new and challenging situations arise. Neurodynamic programming can solve stochastic optimal control and estimation problems in real time, for any form of nonlinear dynamics and performance functions. But, the current formalisms for artificial neural networks and gradient-based learning are far removed from the mechanisms found in biological brains. By integrating theory and experiments, this project will develop neurodynamic programming algorithms that are physiologically plausible and testable on light-sensitive hippocampal and cortical neurons growing in culture. In this experimental setup, the cultured neurons can be trained by light patterns, and it is possible to make defined lesions in the network and evaluate how memory retention is restored, as well as control the connectivity between isolated networks of neurons. By overcoming the complexities that are known to limit such system-level studies in in vitro or in vivo experiments on animals, it is expected that we will uncover the abilities of neuronal networks to store and retrieve sensory information, as well as understand the effect that reward pathways have on this process. The broader impact of this project is to reverse-engineer dopamine and cortical neuronal cultures on a chip, and to help uncover the mechanisms underlying sensorimotor learning in the mammalian brain
