EFRI-COPN: Dynamics of Neural Networks on a Planar Patch-Clamp Array: Training, Identification, and Control

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The core of this project will be a collaboration between Sebastian Seung, who has been using in vitro cultures to test his existing models of learning in neurons, and Tedrake, who has been working on intelligent robotics informed by control theory. The main ideas are: (1) to revolutionize the in vitro work, by solving technical problems with patch-clamp arrays, so that they can be used to monitor hundreds of cells effectively in parallel; and (2) to apply robust system identification to develop new reduced models of the living neural circuits (as well as calibrate traditional biological models), and train these circuits to address benchmark challenges which represent the cutting edge of research in robotics. The patch clamp arrays and the data which they generate may themselves be highly transformative. Solving these technical problems is a major step towards being able to interface with hundreds of neurons in the brain itself, in the future. The connection between control theory approaches, reinforcement learning and actual biological data, in facing common robotic control challenges, will encourage greater crossdisciplinary cooperation and understanding at an institution which plans a key role in US engineering education. The level of detail of this data will make it possible to evaluate models of learning in far more detail than is possible when data are available only on inputs and outputs of the neural circuits.