Analysis and Design of Cultured Neuronal Networks for Adaptive and Reconfigurable Control

用于自适应和可重构控制的培养神经元网络的分析和设计Silvia Ferrari，Craig Henriquez和Antonius M.J.VanDongen Duke大学拟议活动的目标是开发一种训练培养神经元网络的方法，以解决最优控制和预测中的挑战性问题。工程系统，如航空航天和机器人系统，已经受益于反馈控制器和估计器，这些控制器和估计器是人为设计的，用于处理系统动力学先验已知的正常运行条件。然而，这些设计还不能处理涉及高度非线性和先验未知的未建模动态的不可预见的损害和故障。生物系统能够在受到各种限制的情况下以最佳方式运行，并在新的和具有挑战性的情况出现时实时学习和适应。神经动态规划可以实时地解决任意形式的非线性动力学和性能函数的随机最优控制和估计问题。但是，目前人工神经网络和基于梯度的学习的形式主义与生物大脑中的机制相去甚远。通过理论和实验的结合，该项目将开发在培养中生长的光敏感的海马神经元和大脑皮层神经元上具有生理可行性和可测试性的神经动力学编程算法。在这个实验装置中，培养的神经元可以通过光模式进行训练，可以在网络中进行明确的损伤，评估如何恢复记忆保持，以及控制孤立的神经元网络之间的连接。通过克服在体外或体内动物实验中限制这种系统水平研究的复杂性，我们有望发现神经网络存储和检索感觉信息的能力，以及了解奖励通路对这一过程的影响。该项目的更广泛影响是在芯片上对多巴胺和皮质神经元培养进行反向工程，并帮助揭示哺乳动物大脑中潜在的感觉运动学习机制。

Analysis and Design of Cultured Neuronal Networks for Adaptive and Reconfigurable ControlSilvia Ferrari, Craig Henriquez, and Antonius M.J. VanDongenDuke UniversityThe 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