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使用快速序列视觉呈现法研究猕猴颞下神经元的形状调谐特性。

Properties of shape tuning of macaque inferior temporal neurons examined using rapid serial visual presentation.

作者信息

De Baene Wouter, Premereur Elsie, Vogels Rufin

机构信息

Laboratorium voor Neuro- en Psychofysiologie, K.U. Leuven Medical School, Campus Gasthuisberg, Herestraat 49, bus 1021, Leuven, B-3000, Belgium.

出版信息

J Neurophysiol. 2007 Apr;97(4):2900-16. doi: 10.1152/jn.00741.2006. Epub 2007 Jan 24.

Abstract

We used rapid serial visual presentation (RSVP) to examine the tuning of macaque inferior temporal cortical (IT) neurons to five sets of 25 shapes each that varied systematically along predefined shape dimensions. A comparison of the RSVP technique using 100-ms presentations with that using a longer duration showed that shape preference can be determined with RSVP. Using relatively complex shapes that vary along relatively simple shape dimensions, we found that the large majority of neurons preferred extremes of the shape configuration, extending the results of a previous study using simpler shapes and a standard testing paradigm. A population analysis of the neuronal responses demonstrated that, in general, IT neurons can represent the similarities among the shapes at an ordinal level, extending a previous study that used a smaller number of shapes and a categorization task. However, the same analysis showed that IT neurons do not faithfully represent the physical similarities among the shapes. The responses to the two-part shapes could be predicted, virtually perfectly, from the average of the responses to the respective two parts presented in isolation. We also showed that IT neurons adapt to the stimulus distribution statistics. The neural shape discrimination improved when a shape set with a narrower stimulus range was presented, suggesting that the tuning of IT neurons is not static but adapts to the stimulus distribution statistics, at least when stimulated at a high rate with a restricted set of stimuli.

摘要

我们使用快速序列视觉呈现(RSVP)来研究猕猴颞下皮质(IT)神经元对五组形状的调谐,每组有25个形状,这些形状沿着预定义的形状维度系统地变化。将使用100毫秒呈现的RSVP技术与使用更长持续时间的技术进行比较,结果表明形状偏好可以通过RSVP来确定。使用沿着相对简单的形状维度变化的相对复杂的形状,我们发现绝大多数神经元偏好形状配置的极端情况,扩展了先前一项使用更简单形状和标准测试范式的研究结果。对神经元反应的群体分析表明,一般来说,IT神经元可以在序数水平上表征形状之间的相似性,扩展了先前一项使用较少形状数量和分类任务的研究。然而,同样的分析表明,IT神经元并不能如实地表征形状之间的物理相似性。对两部分形状的反应几乎可以完美地从对单独呈现的各个两部分的反应平均值中预测出来。我们还表明,IT神经元会适应刺激分布统计。当呈现具有较窄刺激范围的形状集时,神经形状辨别能力得到改善,这表明IT神经元的调谐不是静态的,而是会适应刺激分布统计,至少在以高频率用一组受限的刺激进行刺激时是这样。

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