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通过反卷积方法从诱发的和微小的突触后反应评估量子神经分泌。

Evaluation of quantal neurosecretion from evoked and miniature postsynaptic responses by deconvolution method.

作者信息

Vorobieva O N, Hackett J T, Worden M K, Bykhovskaia M

机构信息

Department of Molecular Physiology and Biological Physics, University of Virginia, Health Sciences Center, Charlottesville 22906-0011, USA.

出版信息

J Neurosci Methods. 1999 Oct 15;92(1-2):91-9. doi: 10.1016/s0165-0270(99)00101-6.

Abstract

A new deconvolution algorithm has been developed for evaluation of quantal content and its variability at high-output synapses. The algorithm derives the distribution of the number of neurosecretory quanta released in a trial (M) from the measured sizes of evoked postsynaptic responses. The deconvolution employs the distribution of quantal sizes obtained by measuring sizes of miniature postsynaptic responses. The distribution of quantal content M is derived by ridge regression method from the distributions of sizes of the responses and of quantal sizes. The deconvolution method was applied to postsynaptic responses from the excitory innervation of lobster dactyl opener muscle obtained by focal extracellular recordings. The obtained solution (distribution of M) had six to eight components and was stable. The method was tested by the analysis of simulated multiquantal responses. For the simulated responses, the ridge regression solution reproduced the imposed distribution of M within the limits of the calculated confidence intervals. To further test the algorithm, the distribution of M at a low-output synapse was obtained both by deconvolution method and by the method of direct quantal counts. The results of these two methods were found to be in a very good agreement.

摘要

一种新的反卷积算法已被开发出来,用于评估高输出突触处的量子含量及其变异性。该算法从测量的诱发突触后反应大小中推导试验中释放的神经分泌量子数量(M)的分布。反卷积采用通过测量微小突触后反应大小获得的量子大小分布。量子含量M的分布通过岭回归方法从反应大小和量子大小的分布中推导得出。该反卷积方法应用于通过局部细胞外记录获得的龙虾指节 opener 肌肉兴奋性神经支配的突触后反应。获得的解(M的分布)有六到八个成分且稳定。该方法通过对模拟多量子反应的分析进行了测试。对于模拟反应,岭回归解在计算的置信区间范围内重现了施加的M分布。为了进一步测试该算法,通过反卷积方法和直接量子计数方法获得了低输出突触处M的分布。发现这两种方法的结果非常一致。

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