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

这个项目的核心将是Sebastian Seung和Tedrake之间的合作，Sebastian Seung一直在使用体外培养来测试他现有的神经元学习模型，Tedrake一直在研究受控制理论启发的智能机器人。其主要思想是：(1)通过解决膜片钳阵列的技术问题，使体外工作发生革命性的变化，使其能够有效地并行监测数百个细胞；(2)应用稳健的系统辨识，开发新的活神经电路简化模型(以及校准传统生物模型)，并对这些电路进行训练，以应对代表机器人研究前沿的基准挑战。膜片钳阵列及其产生的数据本身可能具有很强的变革性。解决这些技术问题是朝着未来能够与大脑本身的数百个神经元连接的重要一步。在面对常见的机器人控制挑战时，控制理论方法、强化学习和实际生物数据之间的联系将鼓励这家计划在美国工程教育中扮演关键角色的机构进行更多跨学科合作和理解。这些数据的详细程度将使评估学习模型变得比仅在神经电路的输入和输出上获得数据时更详细。

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.