Clewley Robert
Neuroscience Institute and Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, USA.
J Comput Neurosci. 2011 Apr;30(2):391-408. doi: 10.1007/s10827-010-0267-y. Epub 2010 Aug 18.
This work presents a neuroinformatic method for deriving mechanistic descriptions of fine-structured neural activity. This is a new development in the computer-assisted analysis of dynamics in conductance-based models, which is illustrated using single compartment models of an action potential. A sequence of abstract, qualitative motifs is inferred from this analysis, forming a template that is independent of the specific equations from which they were abstracted. The template encodes the assumptions behind the model reduction steps used to derive the motifs, and so specifies quantitative information about their domains of validity. The template representation of a mechanism is converted to a hybrid dynamical system, which is simulated as a sequence of low-dimensional reduced models (in this example, phase plane models) with appropriate switching conditions taken from the motifs. We demonstrate the validity of the template on a detailed single neuron model of spiking taken from the literature, and show that the corresponding hybrid system simulation closely mimics the spiking dynamics of the full model.
这项工作提出了一种神经信息学方法，用于推导精细结构神经活动的机制描述。这是基于电导模型的动力学计算机辅助分析中的一项新进展，通过动作电位的单室模型进行说明。从该分析中推断出一系列抽象的定性基序，形成一个独立于从中抽象出这些基序的特定方程的模板。该模板对用于推导基序的模型简化步骤背后的假设进行编码，从而指定有关其有效性域的定量信息。机制的模板表示被转换为一个混合动态系统，该系统被模拟为一系列具有从基序中获取的适当切换条件的低维简化模型（在本示例中为相平面模型）。我们在从文献中获取的详细的单个神经元发放模型上证明了模板的有效性，并表明相应的混合系统模拟紧密模仿了完整模型的发放动力学。
