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Neuronal Diversity: Categorizing Types of Neurons: Categorically distinct types of receptive fields in early visual cortex

机译:神经元多样性:分类神经元类型:早期视觉皮层中感受野的分类不同类型

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摘要

In the visual cortex, distinct types of neurons have been identified based on cellular morphology, response to injected current, or expression of specific markers, but neurophysiological studies have revealed visual receptive field (RF) properties that appear to be on a continuum, with only two generally recognized classes: simple and complex. Most previous studies have characterized visual responses of neurons using stereotyped stimuli such as bars, gratings, or white noise and simple system identification approaches (e.g., reverse correlation). Here we estimate visual RF models of cortical neurons using visually rich natural image stimuli and regularized regression system identification methods and characterize their spatial tuning, temporal dynamics, spatiotemporal behavior, and spiking properties. We quantitatively demonstrate the existence of three functionally distinct categories of simple cells, distinguished by their degree of orientation selectivity (isotropic or oriented) and the nature of their output nonlinearity (expansive or compressive). In addition, these three types have differing average values of several other properties. Cells with nonoriented RFs tend to have smaller RFs, shorter response durations, no direction selectivity, and high reliability. Orientation-selective neurons with an expansive output nonlinearity have Gabor-like RFs, lower spontaneous activity and responsivity, and spiking responses with higher sparseness. Oriented RFs with a compressive nonlinearity are spatially nondescript and tend to show longer response latency. Our findings indicate multiple physiologically defined types of RFs beyond the simple/complex dichotomy, suggesting that cortical neurons may have more specialized functional roles rather than lying on a multidimensional continuum.
机译:在视觉皮层中,已根据细胞形态,对注入电流的反应或特定标记物的表达鉴定出了不同类型的神经元,但神经生理学研究表明,视觉感受器(RF)特性似乎是连续的,仅两种公认的类:简单和复杂。先前的大多数研究都使用定型刺激(例如条形,光栅或白噪声)和简单的系统识别方法(例如反向相关)来表征神经元的视觉反应。在这里,我们使用视觉上丰富的自然图像刺激和规则化的回归系统识别方法来估计皮质神经元的视觉RF模型,并表征它们的空间调整,时间动态,时空行为和峰值特性。我们定量地证明了简单细胞的三种功能上不同的类别的存在,这些类别的区别在于它们的方向选择性(各向同性或定向)程度和其输出非线性(膨胀或压缩)的性质。另外,这三种类型在其他几个属性上具有不同的平均值。具有非定向RF的单元往往具有较小的RF,较短的响应时间,无方向选择性和高可靠性。具有广泛的输出非线性的定向选择神经元具有类似Gabor的RF,具有较低的自发活动和响应能力,并且具有较高的稀疏性。具有压缩非线性的定向RF在空间上是无法描述的,并且倾向于显示更长的响应等待时间。我们的发现表明,除了简单/复杂的二分法外,RF还具有多种生理学定义的类型,这表明皮质神经元可能具有更特殊的功能作用,而不是躺在多维连续体上。

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