Functional Phase Response Curves: A Method for Understanding Synchronization of Adapting Neurons
Functional Phase Response Curves: A Method for Understanding Synchronization of Adapting Neurons
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DOI：
10.1152/jn.00037.2009
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发表时间：
2009-07-01
影响因子：
2.5
通讯作者：
Butera, Robert J.
中科院分区：
文献类型：
--
作者：
Cui, Jianxia;Canavier, Carmen C.;Butera, Robert J.
崔J，Canavier CC，Butera R.功能性相位响应曲线：理解适应神经元同步化的方法。J Neurophysiol 102：387-398，2009.首次发表于2009年5月6日; doi：10.1152/jn.00037.2009。单个神经元的相位响应曲线（PRCs）通常用于预测相互耦合的神经元的同步性。以前的脉冲耦合振荡器的理论工作使用单脉冲微扰。我们提出了一种替代的方法，其中功能PRCs（fPRCs）使用的脉冲施加在一个固定的延迟后，每个尖峰，与PRC测量时，刺激和随后的尖峰之间的相位关系的神经元已经收敛。基本信息是从脉冲开始直到下一个尖峰的恢复时间的依赖性，作为前一个尖峰和所施加的脉冲的开始之间的延迟的函数。实验性fPRCs在Astrassia起搏神经元不同于单脉冲PRCs，主要是由于适应。在生物神经元中，收敛到完全适应的恢复间隔在某些阶段比在其他阶段慢，因为适应引起的有效固有周期的变化以对抗和减缓适应效应的方式改变了有效相位重置。利用两个孤立的适应模型神经元的fPRC预测了两个神经元耦合时1：1锁相网络活动的存在性和稳定性。通过线性化一个基于fPRC的耦合映射，得到了一个稳定性判据，并在具有相互抑制或相互激励的双模拟神经元网络中成功地检验了该判据的存在性和稳定性. fPRC是第一个基于PRC的工具，可以解释分析神经振荡器网络的适应性。
Cui J, Canavier CC, Butera R. Functional phase response curves: a method for understanding synchronization of adapting neurons. J Neurophysiol 102: 387-398, 2009. First published May 6, 2009; doi:10.1152/jn.00037.2009. Phase response curves (PRCs) for a single neuron are often used to predict the synchrony of mutually coupled neurons. Previous theoretical work on pulse-coupled oscillators used single-pulse perturbations. We propose an alternate method in which functional PRCs (fPRCs) are generated using a train of pulses applied at a fixed delay after each spike, with the PRC measured when the phasic relationship between the stimulus and the subsequent spike in the neuron has converged. The essential information is the dependence of the recovery time from pulse onset until the next spike as a function of the delay between the previous spike and the onset of the applied pulse. Experimental fPRCs in Aplysia pacemaker neurons were different from single-pulse PRCs, principally due to adaptation. In the biological neuron, convergence to the fully adapted recovery interval was slower at some phases than that at others because the change in the effective intrinsic period due to adaptation changes the effective phase resetting in a way that opposes and slows the effects of adaptation. The fPRCs for two isolated adapting model neurons were used to predict the existence and stability of 1: 1 phase-locked network activity when the two neurons were coupled. A stability criterion was derived by linearizing a coupled map based on the fPRC and the existence and stability criteria were successfully tested in two-simulated-neuron networks with reciprocal inhibition or excitation. The fPRC is the first PRC-based tool that can account for adaptation in analyzing networks of neural oscillators.