REDUCTION OF CONDUCTANCE-BASED NEURON MODELS
REDUCTION OF CONDUCTANCE-BASED NEURON MODELS
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DOI：
10.1007/bf00197717
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发表时间：
1992-03-01
影响因子：
1.9
通讯作者：
MARDER, E
中科院分区：
文献类型：
--
作者：
KEPLER, TB;ABBOTT, LF;MARDER, E
我们提出了一个方案，系统地减少生物物理现实神经元模型所需的微分方程的数量。这些技术是通用的，旨在适用于大量这样的模型，并在简化的系统中保留尽可能高的对原始系统的保真度。作为例子，我们提供了霍奇金-赫胥黎系统的简化和康纳等人（1977）的a电流模型。
We present a scheme for systematically reducing the number of differential equations required for biophysically realistic neuron models. The techniques are general, are designed to be applicable to a large set of such models and retain in the reduced system as high a degree of fidelity to the original system as possible. As examples, we provide reductions of the Hodgkin-Huxley system and the A-current model of Connor et al. (1977).