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局部神经元集合中视觉信息整合的比较分析。

A comparative analysis of integrating visual information in local neuronal ensembles.

机构信息

Department of Mathematics and Statistics, University of Victoria, Victoria, BC V8W 3R4 Canada.

出版信息

J Neurosci Methods. 2012 May 30;207(1):23-30. doi: 10.1016/j.jneumeth.2012.03.008. Epub 2012 Mar 28.

Abstract

Spike directivity, a new measure that quantifies the transient charge density dynamics within action potentials provides better results in discriminating different categories of visual object recognition. Specifically, intracranial recordings from medial temporal lobe (MTL) of epileptic patients have been analyzed using firing rate, interspike intervals and spike directivity. A comparative statistical analysis of the same spikes from a local ensemble of four selected neurons shows that electrical patterns in these neurons display higher separability to input images compared to spike timing features. If the observation vector includes data from all four neurons then the comparative analysis shows a highly significant separation between categories for spike directivity (p=0.0023) and does not display separability for interspike interval (p=0.3768) and firing rate (p=0.5492). Since electrical patterns in neuronal spikes provide information regarding different presented objects this result shows that related information is intracellularly processed in neurons and carried out within a millisecond-level time domain of action potential occurrence. This significant statistical outcome obtained from a local ensemble of four neurons suggests that meaningful information can be electrically inferred at the network level to generate a better discrimination of presented images.

摘要

棘波方向性是一种新的度量方法,可量化动作电位内的瞬态电荷量密度动态,在区分不同类别的视觉目标识别方面提供了更好的结果。具体来说,使用放电率、棘波间隔和棘波方向性对癫痫患者内侧颞叶 (MTL) 的颅内记录进行了分析。对来自四个选定神经元的局部集合的相同棘波进行的比较统计分析表明,与棘波时间特征相比,这些神经元中的电模式对输入图像显示出更高的可分离性。如果观察向量包括来自所有四个神经元的数据,则比较分析显示,棘波方向性(p=0.0023)之间具有高度显著的类别分离,而棘波间隔(p=0.3768)和放电率(p=0.5492)不具有可分离性。由于神经元棘波中的电模式提供了有关不同呈现物体的信息,因此这一结果表明,相关信息在神经元内被细胞内处理,并在动作电位发生的毫秒级时间域内进行。从四个神经元的局部集合获得的这一显著统计结果表明,可以在网络级别进行电推断以生成对呈现图像的更好区分。

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