Prinz Astrid A
Department of Biology, Emory University, Atlanta, GA, USA.
Methods Mol Biol. 2007;401:167-79. doi: 10.1007/978-1-59745-520-6_10.
The electrical activity of individual neurons and neuronal networks is shaped by the complex interplay of a large number of non-linear processes, including the voltage-dependent gating of ion channels and the activation of synaptic receptors. These complex dynamics make it difficult to understand how individual neuron or network parameters-such as the number of ion channels of a given type in a neuron's membrane or the strength of a particular synapse-influence neural system function. Systematic exploration of cellular or network model parameter spaces by computational brute force can overcome this difficulty and generate comprehensive data sets that contain information about neuron or network behavior for many different combinations of parameters. Searching such data sets for parameter combinations that produce functional neuron or network output provides insights into how narrowly different neural system parameters have to be tuned to produce a desired behavior. This chapter describes the construction and analysis of databases of neuron or neuronal network models and describes some of the advantages and downsides of such exploration methods.
单个神经元和神经元网络的电活动是由大量非线性过程的复杂相互作用所塑造的，这些过程包括离子通道的电压依赖性门控和突触受体的激活。这些复杂的动力学使得理解单个神经元或网络参数（例如神经元膜中给定类型离子通道的数量或特定突触的强度）如何影响神经系统功能变得困难。通过计算蛮力对细胞或网络模型参数空间进行系统探索可以克服这一困难，并生成包含许多不同参数组合下神经元或网络行为信息的综合数据集。在这些数据集中搜索产生功能性神经元或网络输出的参数组合，有助于深入了解为产生期望行为，神经系统参数的微调范围有多窄。本章描述了神经元或神经元网络模型数据库的构建与分析，并介绍了此类探索方法的一些优缺点。
