At the level of individual neurons, various coding properties can be inferred from the input-output relationship of a cell. For small inputs, this relation is captured by the phase-response curve (PRC), which measures the effect of a small perturbation on the timing of the subsequent spike. Experimentally, however, an accurate experimental estimation of PRCs is challenging. Despite elaborate measurement efforts, experimental PRC estimates often cannot be related to those from modeling studies. In particular, experimental PRCs rarely resemble the characteristic theoretical PRC expected close to spike initiation, which is indicative of the underlying spike-onset bifurcation. Here, we show for conductance-based model neurons that the correspondence between theoretical and measured phase-response curve is lost when the stimuli used for the estimation are too large. In this case, the derived phase-response curve is distorted beyond recognition and takes on a generic shape that reflects the measurement protocol and masks the spike-onset bifurcation. We discuss how to identify appropriate stimulus strengths for perturbation and noise-stimulation methods, which permit to estimate PRCs that reliably reflect the spike-onset bifurcation – a task that is particularly difficult if a lower bound for the stimulus amplitude is dictated by prominent intrinsic neuronal noise.

中文翻译：

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如何从嘈杂的录音中正确量化神经元的相位响应曲线。

在单个神经元的水平上，可以从细胞的输入-输出关系推断出各种编码特性。对于小输入，该关系由相位响应曲线（PRC）捕获，该曲线测量了小扰动对后续尖峰定时的影响。然而，从实验上来说，对PRC进行准确的实验估算是一项挑战。尽管进行了详尽的衡量工作，但实验性PRC估算通常与建模研究的估算值无关。特别是，实验性PRCs很少类似于预期接近峰值起始的特征性PRC，这表明潜在的峰值发作分叉。这里，我们为基于电导的模型神经元显示，当用于估计的刺激太大时，理论和实测相位响应曲线之间的对应关系将丢失。在这种情况下，导出的相位响应曲线会扭曲到无法识别的程度，并呈现出反映测量协议并掩盖尖峰发作分叉的通用形状。我们将讨论如何为扰动和噪声刺激方法确定合适的刺激强度，从而可以估计可靠反映尖峰发作分叉的PRCs-如果刺激幅度的下限由突出的内在神经元决定，则这一任务尤其困难噪声。得出的相位响应曲线会失真得无法识别，并呈现出可反映测量协议并掩盖尖峰开始分叉的通用形状。我们将讨论如何为扰动和噪声刺激方法确定合适的刺激强度，从而可以估计可靠反映尖峰发作分叉的PRCs-如果刺激幅度的下限由突出的内在神经元决定，则这一任务尤其困难噪声。得出的相位响应曲线会失真得无法识别，并呈现出可反映测量协议并掩盖尖峰开始分叉的通用形状。我们将讨论如何为扰动和噪声刺激方法确定合适的刺激强度，从而可以估计可靠反映尖峰发作分叉的PRCs-如果刺激幅度的下限由突出的内在神经元决定，则这一任务尤其困难噪声。