Can mammalian pattern generators be understood?
Can mammalian pattern generators be understood?
复制标题
哺乳动物的模式生成器可以被理解吗？
DOI：
10.1017/s0140525x00006713
复制
发表时间：
1980
影响因子：
29.3
通讯作者：
Wesley Thompson
中科院分区：
文献类型：
--
作者：
James L. Larimer;Wesley Thompson
大多数节律性行为是由中枢神经系统中发现的一组特殊的神经元集合产生的。这些中枢模式发生器（CPGs）已经成为神经元电路分析的基石。研究简单的无脊椎动物神经系统可以揭示参与产生节律性运动输出的神经元之间的相互作用。最近在这一领域取得了一些进展，但由于cpg的某些内在特征，目前的技术不太可能完全理解其中最简单的内容。主要的障碍似乎是我们无法识别和描述构成CPG的整个神经元间池。此外，我们的一般分析策略依赖于描述性的、还原论的方法，除了现象学建模之外没有分析结构。详细的描述性数据对于特定的模型测试来说通常没有足够的深度，从而导致对机制的特别解释，而这些解释通常是不正确的。因为他们做了太多的假设，建模研究并没有增加我们对cpc的理解；这与其说是由于不充分的模拟，不如说是由于实验人员提供的数据质量差和不完整。为神经建模提供足够信息的基本策略包括：(1)识别和表征CPG网络中的每个元素；(2)明确这些元素之间的突触连接；(3)利用连接矩阵分析非线性突触性质和相互作用。还讨论了基于我们目前技术能力的局限性。
Abstract Most rhythmic behaviors are produced by a specialized ensemble of neurons found in the central nervous system. These central pattern generators (CPGs) have become a cornerstone of neuronal circuit analysis. Studying simple invertebrate nervous systems may reveal the interactions of the neurons involved in the production of rhythmic motor output. There has recently been progress in this area, but due to certain intrinsic features of CPGs it is unlikely that present techniques will ever yield a complete understanding of any but the simplest of them. The chief impediment seems to be our inability to identify and characterize the total interneuronal pool making up a CPG. In addition, our general analytic strategy relies on a descriptive, reductionist approach, with no analytical constructs beyond phenomenological modeling. Detailed descriptive data are usually not of sufficient depth for specific model testing, giving rise instead to ad hoc explanations of mechanisms which usually turn out to be incorrect. Because they make too many assumptions, modeling studies have not added much to our understanding of CPCs; this is due not so much to inadequate simulations as to the poor quality and incomplete nature of the data provided by experimentalists. A basic strategy that would provide sufficient information for neural modeling would include: (1) identifying and characterizing each element in the CPG network; (2) specifying the synaptic connectivity between the elements; and (3) analyzing nonlinear synaptic properties and interactions by means of the connectivity matrix. Limitations based on our present technical capabilities are also discussed.
影响因子：
56.9
作者：
DELCOMYN, F
通讯作者：
DELCOMYN, F
DOI：
--
发表时间：
1978-06
期刊：
The Journal of Experimental Biology
影响因子：
--
作者：
J. Truman
通讯作者：
J. Truman