Suppr超能文献

Morphological correlates of intrinsic electrical excitability in neurons of the deep cerebellar nuclei.

作者信息

Aizenman Carlos D, Huang Eric J, Linden David J

机构信息

Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.

出版信息

J Neurophysiol. 2003 Apr;89(4):1738-47. doi: 10.1152/jn.01043.2002.

Abstract

To what degree does neuronal morphology determine or correlate with intrinsic electrical properties within a particular class of neuron? This question has been examined using microelectrode recordings and subsequent neurobiotin filling and reconstruction of neurons in the deep cerebellar nuclei (DCN) of brain slices from young rats (P13-16). The neurons reconstructed from these recordings were mostly large and multipolar (17/21 cells) and were likely to represent glutamatergic projection neurons. Within this class, there was considerable variation in intrinsic electrical properties and cellular morphology. Remarkably, in a correlation matrix of 18 electrophysiological and 6 morphological measures, only one morphological characteristic was predictive of intrinsic excitability: neurons with more spines had a significantly slower basal firing rate. To address the possibility that neurons with fewer spines represented a slowly maturing subgroup, recordings and reconstructions were also made from neurons at a younger age (P6-9). While P6-9 neurons were morphologically indistinguishable from P13 to 16 neurons, they were considerably less excitable: P6-9 neurons had a lower spontaneous spiking rate, larger fast AHPs, higher resting membrane potentials, and smaller rebound depolarizations. Thus while the large projection neurons of the DCN are morphologically mature by P6-9, they continue to mature electrophysiologically through P13-16 in a way that renders them more responsive to the burst-and-pause pattern that characterizes Purkinje cell inhibitory synaptic drive.

摘要

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验