Single neuron computation: From dynamical system to feature detector
Single neuron computation: From dynamical system to feature detector
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DOI：
10.1162/neco.2007.19.12.3133
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发表时间：
2007-12-01
影响因子：
2.9
通讯作者：
Fairhall, Adrienne L.
中科院分区：
文献类型：
--
作者：
Hong, Sungho;Aguera y Arcas, Blaise;Fairhall, Adrienne L.
白色噪声方法是描述神经系统计算特性的有力工具。这些方法允许人们识别神经系统从复杂输入中提取的特征，并确定这些特征如何组合以驱动系统的尖峰响应。这些方法也被应用到表征单神经元的突触输入驱动的输入输出关系，模拟直流注入。为了解释单个神经元的白色噪声分析结果，我们想了解获得的单个神经元的特征空间如何映射到膜的生物物理特性，特别是离子通道的动力学。在这里，通过一个简单的动力学模型神经元的分析，我们绘制了明确的连接之间的输出的白色噪声分析和底层的动力系统。我们发现，在一定的假设下，相关功能的形式是很好地定义的动力系统的参数。此外，我们表明，在某些条件下，特征空间是由尖峰触发的平均值和连续顺序的时间导数。
White noise methods are a powerful tool for characterizing the computation performed by neural systems. These methods allow one to identify the feature or features that a neural system extracts from a complex input and to determine how these features are combined to drive the system's spiking response. These methods have also been applied to characterize the input-output relations of single neurons driven by synaptic inputs, simulated by direct current injection. To interpret the results of white noise analysis of single neurons, we would like to understand how the obtained feature space of a single neuron maps onto the biophysical properties of the membrane, in particular, the dynamics of ion channels. Here, through analysis of a simple dynamical model neuron, we draw explicit connections between the output of a white noise analysis and the underlying dynamical system. We find that under certain assumptions, the form of the relevant features is well defined by the parameters of the dynamical system. Further, we show that under some conditions, the feature space is spanned by the spike-triggered average and its successive order time derivatives.