Controlling Populations of Neurons

控制神经元群体

• 批准号: 1000678
• 负责人: Jeffrey Moehlis
• 金额: $ 45万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1000678&HistoricalAwards=false
• 关键词: Controlling; Populations; Neurons

The proposed research will use engineering techniques, mathematical principles, and computer simulations to understand how pathological neural synchronization associated with Parkinson's disease can be disrupted through the injection of current stimuli. This includes determining control inputs which optimally reset a neuron's phase by driving the state of the neuron to a phaseless set at which it is very sensitive to noise. When neurons in a population receive such a common stimulus, the noise serves to randomize the neurons' phases, thereby desychronizing the population. In this control scheme, such inputs will be triggered by the detection of population-level synchrony, giving an event-based feedback control algorithm for which the biological tissue is only stimulated when necessary, thereby reducing the overall accumulation of negative side effects of electrical stimulation, and also the amount of power used. In an alternative approach, nonlinear hybrid control, involving the application of a series of different control laws, will be used to break neural synchrony by stabilizing the splay state for the coupled neuron system, which is the state for which the neurons' phases are evenly distributed. Such control will also be generalized to an event-based framework, and optimized so that it minimizes the total input energy or the total time needed to reach the splay state. The robustness of the control algorithms will be explored with respect to uncertainties in the neuron model, uncertainties in the measurements of the firing events, different types of coupling, changes to the input stimulus, heterogeneity of the stimulus due to proximity to the electrode, and properties of the noise.There is evidence that the tremors associated with Parkinson's disease are associated with the pathological synchronization of neurons in the motor control region of the patient's brain. An FDA-approved treatment for such tremors, called deep brain stimulation, involves the implantation of an electrode into this region, which is used to inject electrical current into the brain tissue. As presently implemented, the electrical current is typically a periodic sequence of pulses with a frequency around 100 Hertz, which has been shown empirically to be an effective treatment for some patients. This research will use engineering techniques, mathematical principles, and state-of-the-art computer simulations to develop the theoretical foundation for alternative electrical current stimuli for deep brain stimulation, which could lead to better treatments for Parkinson's disease. In particular, control algorithms will be developed which minimize the amount of current injected, which will minimize tissue damage and energy consumption, the latter reducing the need for surgery to replace the battery which is used for deep brain stimulation. This will also include the use of feedback control, in which the state of the neural population is monitored by an electrode and the electrical current stimulus is only injected as needed.

这项拟议的研究将使用工程技术、数学原理和计算机模拟来了解与帕金森氏症相关的病理性神经同步如何通过注入电流刺激来扰乱。这包括确定控制输入，通过将神经元的状态驱动到对噪声非常敏感的无相设置来最佳地重置神经元的相位。当一个群体中的神经元收到这样一个共同的刺激时，噪声会使神经元的相位随机化，从而使群体失去同步性。在该控制方案中，这种输入将通过检测种群级别的同步来触发，从而给出了基于事件的反馈控制算法，对于该算法，仅在必要时才刺激生物组织，从而减少了电刺激的负面副作用的总体积累，并减少了所使用的功率。在另一种方法中，非线性混合控制涉及一系列不同的控制律的应用，将通过稳定耦合神经元系统的展开状态来打破神经同步，该状态是神经元的相位均匀分布的状态。这样的控制也将被推广到基于事件的框架，并进行优化，以使达到展开状态所需的总输入能量或总时间最小化。控制算法的稳健性将从神经元模型的不确定性、放电事件测量的不确定性、不同类型的耦合、输入刺激的变化、由于接近电极的刺激的异质性以及噪声的特性来探索。有证据表明，与帕金森氏病相关的震颤与患者大脑运动控制区神经元的病理同步有关。FDA批准的一种治疗这种震颤的方法称为脑深部刺激，涉及在该区域植入电极，用于向脑组织注入电流。正如目前实施的那样，电流通常是频率在100赫兹左右的周期性脉冲序列，这已经被经验证明对一些患者是有效的治疗。这项研究将利用工程技术、数学原理和最先进的计算机模拟来开发用于脑深部刺激的替代电流刺激的理论基础，这可能会导致更好地治疗帕金森氏症。特别是，将开发控制算法，将注入的电流量降至最低，从而将组织损伤和能量消耗降至最低，后者减少了手术更换用于脑深部刺激的电池的需要。这也将包括反馈控制的使用，在反馈控制中，神经群体的状态由电极监控，电流刺激仅在需要时注入。