Schiff Steven J
Department of Engineering Science, Pennsylvania State University, University Park, PA 16802, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3318-21. doi: 10.1109/IEMBS.2009.5333752.
Since the 1950s, we have developed mature theories of modern control theory and computational neuroscience with almost no interaction between these disciplines. With the advent of computationally efficient nonlinear Kalman filtering techniques, along with improved neuroscience models that provide increasingly accurate reconstruction of dynamics in a variety of important normal and disease states in the brain, the prospects for a synergistic interaction between these fields are now strong. I show recent examples of the use of nonlinear control theory for the assimilation and control of single neuron dynamics, the modulation of oscillatory wave dynamics in brain cortex, a control framework for Parkinsonian dynamics and seizures, and the use of optimized parameter model networks to assimilate complex network data - the 'consensus set'.
自20世纪50年代以来，我们已经发展出了成熟的现代控制理论和计算神经科学理论，但这两个学科之间几乎没有互动。随着计算效率高的非线性卡尔曼滤波技术的出现，以及神经科学模型的改进，这些模型能够越来越准确地重建大脑中各种重要的正常和疾病状态下的动力学，现在这两个领域之间产生协同互动的前景非常广阔。我展示了一些近期的例子，包括使用非线性控制理论来同化和控制单个神经元动力学、调节大脑皮层中的振荡波动力学、帕金森病动力学和癫痫发作的控制框架，以及使用优化参数模型网络来同化复杂网络数据——“共识集”。
