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线虫轻拍撤离回路的动态网络模拟:使用行为标准对突触功能的预测。

A dynamic network simulation of the nematode tap withdrawal circuit: predictions concerning synaptic function using behavioral criteria.

作者信息

Wicks S R, Roehrig C J, Rankin C H

机构信息

Program in Neuroscience, University of British Columbia, Vancouver, Canada.

出版信息

J Neurosci. 1996 Jun 15;16(12):4017-31. doi: 10.1523/JNEUROSCI.16-12-04017.1996.

Abstract

The nematode tap withdrawal reflex demonstrates several forms of behavioral plasticity. Although the neural connectivity that supports this behavior is identified (Integration of mechanosensory stimuli in Caenorhabditis elegans, Wicks and Rankin, 1995, J Neurosci 15:2434-2444), the neurotransmitter phenotypes, and hence whether the synapses in the circuit are excitatory or inhibitory, remain uncharacterized. Here we use a novel strategy to predict the polarity configuration, i.e., the array of excitatory and inhibitory connections, of the nematode tap withdrawal circuit using an anatomically and physiologically justifiable dynamic network simulation of that circuit. The output of the modeled circuit was optimized to the behavior of animals, which possessed circuits altered by surgical ablation by exhaustively enumerating an array of synaptic signs that constituted the modeled circuit. All possible polarity configurations were then compared, and a statistical analysis was used to determine whether, for a given synaptic class, a particular polarity was associated with a good fit to behavioral data. The results from four related experiments were used to predict the polarities of seven of the nine cell classes of the tap withdrawal circuit. In addition, the model was used to assess possible roles for two novel mechanosensory integration neurons: DVA and PVD.

摘要

线虫轻拍退缩反射表现出多种行为可塑性形式。尽管支持这种行为的神经连接已被确定(《秀丽隐杆线虫机械感觉刺激的整合》,威克斯和兰金,1995年,《神经科学杂志》15:2434 - 2444),但神经递质表型以及因此该回路中的突触是兴奋性还是抑制性仍未明确。在这里,我们使用一种新策略,通过对该回路进行解剖学和生理学上合理的动态网络模拟,来预测线虫轻拍退缩回路的极性配置,即兴奋性和抑制性连接的阵列。对模拟回路的输出进行优化,使其与动物行为相匹配,这些动物的回路因手术切除而改变,通过详尽列举构成模拟回路的一系列突触符号来实现。然后比较所有可能的极性配置,并使用统计分析来确定对于给定的突触类别,特定的极性是否与行为数据的良好拟合相关。来自四个相关实验的结果被用于预测轻拍退缩回路九个细胞类别中七个类别的极性。此外,该模型还用于评估两个新型机械感觉整合神经元:DVA和PVD的可能作用。

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