Event-based minimum-time control of oscillatory neuron models
Event-based minimum-time control of oscillatory neuron models
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DOI：
10.1007/s00422-009-0344-3
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发表时间：
2009-12-01
影响因子：
1.9
通讯作者：
Moehlis, Jeff
中科院分区：
文献类型：
--
作者：
Danzl, Per;Hespanha, Joao;Moehlis, Jeff
我们提出了一种基于事件的反馈控制方法来随机化振荡神经元的渐近相位。相位随机化是通过将神经元的状态驱动到其无相位设置来实现的，在该点上其相位是不确定的，并且对背景噪声极其敏感。我们考虑了对刺激信号施加固定幅度约束的生物相关情况，并展示了如何在最短的时间内完成控制目标。利用最小时间最优的Hamilton-Jacobi-Bellman框架解决了控制综合问题，该框架具有很强的通用性，可以应用于任何基于电导的Hodgkin-Huxley形式的尖峰神经元模型。我们还使用这种方法计算了一个反馈控制协议，以获得最优的尖峰速率增加。这个框架提供了一种直观的方法来可视化等时线，而不是用传统的方式实际计算它们。最后，我们提出了一种应用于种群水平的相位随机化控制方案的扩展，该方案适用于同步放电的全局耦合神经元网络。所施加的控制信号以需求控制的方式使人口去同步。
We present an event-based feedback control method for randomizing the asymptotic phase of oscillatory neurons. Phase randomization is achieved by driving the neuron's state to its phaseless set, a point at which its phase is undefined and is extremely sensitive to background noise. We consider the biologically relevant case of a fixed magnitude constraint on the stimulus signal, and show how the control objective can be accomplished in minimum time. The control synthesis problem is addressed using the minimum-time-optimal Hamilton-Jacobi-Bellman framework, which is quite general and can be applied to any spiking neuron model in the conductance-based Hodgkin-Huxley formalism. We also use this methodology to compute a feedback control protocol for optimal spike rate increase. This framework provides a straightforward means of visualizing isochrons, without actually calculating them in the traditional way. Finally, we present an extension of the phase randomizing control scheme that is applied at the population level, to a network of globally coupled neurons that are firing in synchrony. The applied control signal desynchronizes the population in a demand-controlled way.