Alternative to hand-tuning conductance-based models: Construction and analysis of databases of model neurons
Alternative to hand-tuning conductance-based models: Construction and analysis of databases of model neurons
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DOI：
10.1152/jn.00641.2003
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发表时间：
2003-12-01
影响因子：
2.5
通讯作者：
Marder, E
中科院分区：
文献类型：
--
作者：
Prinz, AA;Billimoria, CP;Marder, E
传统上，神经元模型的参数是使用试错搜索来手动调整的，以产生期望的行为。在这里，我们提出了一种替代方法。我们已经产生了一个数据库，约170万单室模型神经元，通过独立地改变8个最大的膜电导的基础上测量龙虾口胃神经元。我们用自适应算法对每个模型神经元的自发电活动及其在运行时对输入的响应进行了分类，并保存了每个神经元活动模式的简化版本。我们对8维电导空间中不同活动类型（沉默、尖峰、爆发、不规则）的分布的分析表明，我们选择的电导值的粗网格足以捕获分布的显著特征。该数据库可以搜索神经元特性的不同组合，例如活动类型、尖峰或爆发频率、静息电位、频率-电流关系和相位响应曲线。我们演示了如何可以筛选数据库的模型，再现一个特定的生物神经元的行为，并显示数据库的内容可以让洞察神经元的膜电导确定其活动模式和响应特性的方式。可以构建类似的数据库来探索多室模型或小型网络中的参数空间，或者检查电流的电压依赖性变化的影响。在所有情况下，数据库搜索都可以深入了解神经元和网络属性如何取决于模型中的参数值。
Conventionally, the parameters of neuronal models are hand-tuned using trial-and-error searches to produce a desired behavior. Here, we present an alternative approach. We have generated a database of about 1.7 million single-compartment model neurons by independently varying 8 maximal membrane conductances based on measurements from lobster stomatogastric neurons. We classified the spontaneous electrical activity of each model neuron and its responsiveness to inputs during runtime with an adaptive algorithm and saved a reduced version of each neuron's activity pattern. Our analysis of the distribution of different activity types (silent, spiking, bursting, irregular) in the 8-dimensional conductance space indicates that the coarse grid of conductance values we chose is sufficient to capture the salient features of the distribution. The database can be searched for different combinations of neuron properties such as activity type, spike or burst frequency, resting potential, frequency-current relation, and phase-response curve. We demonstrate how the database can be screened for models that reproduce the behavior of a specific biological neuron and show that the contents of the database can give insight into the way a neuron's membrane conductances determine its activity pattern and response properties. Similar databases can be constructed to explore parameter spaces in multicompartmental models or small networks, or to examine the effects of changes in the voltage dependence of currents. In all cases, database searches can provide insight into how neuronal and network properties depend on the values of the parameters in the models.