Measurement of infinitesimal phase response curves from noisy real neurons
Measurement of infinitesimal phase response curves from noisy real neurons
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DOI：
10.1103/physreve.84.041902
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发表时间：
2011-10-03
影响因子：
2.4
通讯作者：
Aonishi, Toru
中科院分区：
文献类型：
--
作者：
Ota, Keisuke;Omori, Toshiaki;Aonishi, Toru
我们试图测量大鼠海马 CA1 锥体神经元的无穷小相位响应曲线 (iPRC)。由于必须牺牲对外部扰动的响应的线性度或信噪比，因此很难从嘈杂的神经元中测量 iPRC。为了克服这个困难，我们使用朗之万相方程（LPE）形式表示的 iPRC 测量模型来提取贝叶斯方案中的 iPRC。然后，我们通过证明具有估计 iPRC 的 LPE 可以预测已测量其 iPRC 的相同神经元在受到周期性刺激电流扰动时的随机行为，同时验证了测量模型的有效性和估计 iPRC 的可靠性。我们的结果表明，LPE 是真实振荡神经元的有效模型，并且基于它的许多理论框架可能适用于真实的神经系统。
We sought to measure infinitesimal phase response curves (iPRCs) from rat hippocampal CA1 pyramidal neurons. It is difficult to measure iPRCs from noisy neurons because of the dilemma that either the linearity or the signal-to-noise ratio of responses to external perturbations must be sacrificed. To overcome this difficulty, we used an iPRC measurement model formulated as the Langevin phase equation (LPE) to extract iPRCs in the Bayesian scheme. We then simultaneously verified the effectiveness of the measurement model and the reliability of the estimated iPRCs by demonstrating that LPEs with the estimated iPRCs could predict the stochastic behaviors of the same neurons, whose iPRCs had been measured, when they were perturbed by periodic stimulus currents. Our results suggest that the LPE is an effective model for real oscillating neurons and that many theoretical frameworks based on it may be applicable to real nerve systems.