Computation in a single neuron: Hodgkin and Huxley revisited
Computation in a single neuron: Hodgkin and Huxley revisited
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DOI：
10.1162/08997660360675017
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发表时间：
2003-08-01
影响因子：
2.9
通讯作者：
Bialek, W
中科院分区：
文献类型：
--
作者：
Arcas, BAY;Fairhall, AL;Bialek, W
尖峰神经元通过将复杂的动态输入转换为一系列动作电位或尖峰来进行“计算”。由神经元执行的计算可以用公式表示为降维或特征检测，然后是低维空间上的非线性决策函数。具有白色噪声输入的反相关技术的推广提供了从实验数据中提取相关低维特征的数值策略，并且信息论可以用于评估低维逼近的质量。我们应用这些方法来分析最简单的生物病理学现实模型神经元，霍奇金-赫胥黎（HH）模型，使用这个系统来说明一般的方法问题。我们专注于刺激中触发尖峰的功能，明确消除尖峰之间的相互作用的影响。人们可以将这种触发“特征空间”近似为输入历史的高维空间中的二维线性子空间，以这种方式捕获输入和尖峰时间之间的互信息的相当大的一部分。我们发现，一个更好的近似，然而，是描述相关的子空间为二维的，但弯曲的;这样，我们可以捕获90%的互信息，即使在高时间分辨率。我们的分析提供了一个新的理解HH模型的计算特性。虽然将神经行为近似为“整合并激发”是很常见的，但HH模型不是一个整合器，也不是由单个阈值很好地描述的。
A spiking neuron "computes" by transforming a complex dynamical input into a train of action potentials, or spikes. The computation performed by the neuron can be formulated as dimensional reduction, or feature detection, followed by a nonlinear decision function over the low-dimensional space. Generalizations of the reverse correlation technique with white noise input provide a numerical strategy for extracting the relevant low-dimensional features from experimental data, and information theory can be used to evaluate the quality of the low-dimensional approximation. We apply these methods to analyze the simplest biophysically realistic model neuron, the Hodgkin-Huxley (HH) model, using this system to illustrate the general methodological issues. We focus on the features in the stimulus that trigger a spike, explicitly eliminating the effects of interactions between spikes. One can approximate this triggering "feature space" as a two-dimensional linear subspace in the high-dimensional space of input histories, capturing in this way a substantial fraction of the mutual information between inputs and spike time. We find that an even better approximation, however, is to describe the relevant subspace as two dimensional but curved; in this way, we can capture 90% of the mutual information even at high time resolution. Our analysis provides a new understanding of the computational properties of the HH model. While it is common to approximate neural behavior as "integrate and fire," the HH model is not an integrator nor is it well described by a single threshold.