Efficient estimation of phase-resetting curves in real neurons and its significance for neural-network modeling -: art. no. 158101
Efficient estimation of phase-resetting curves in real neurons and its significance for neural-network modeling -: art. no. 158101
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DOI：
10.1103/physrevlett.94.158101
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发表时间：
2005-04-22
影响因子：
8.6
通讯作者：
Urban, NN
中科院分区：
文献类型：
--
作者：
Galán, RF;Ermentrout, GB;Urban, NN
神经振荡器的相位重置曲线（PRC）描述了扰动对其周期运动的影响，因此有助于研究神经元如何响应刺激以及它是否与网络中的其他神经元相锁定。结合理论、计算机模拟和电生理实验，提出了一种估算真实神经元PRC的简单方法。这使我们能够将单个神经元的复杂动态简化为相位模型。我们还说明了如何从估计的PRC推断出相干网络活动的存在。
The phase-resetting curve (PRC) of a neural oscillator describes the effect of a perturbation on its periodic motion and is therefore useful to study how the neuron responds to stimuli and whether it phase locks to other neurons in a network. Combining theory, computer simulations and electrophysiological experiments we present a simple method for estimating the PRC of real neurons. This allows us to simplify the complex dynamics of a single neuron to a phase model. We also illustrate how to infer the existence of coherent network activity from the estimated PRC.