Receptive Field Vectors of Genetically-Identified Retinal Ganglion Cells Reveal Cell-Type-Dependent Visual Functions.
Receptive Field Vectors of Genetically-Identified Retinal Ganglion Cells Reveal Cell-Type-Dependent Visual Functions.
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遗传鉴定的视网膜神经节细胞的接收场载体揭示了细胞型依赖性视觉功能。
DOI：
10.1371/journal.pone.0147738
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Nikolic K
中科院分区：
文献类型：
--
作者：
Katz ML;Viney TJ;Nikolic K
感觉刺激由不同种类的神经元编码，但被研究的记录神经元的身份往往是未知的。我们详细探讨了8个先前定义的遗传鉴定的视网膜神经节细胞（RGC）类型从一个单一的转基因小鼠系的放电模式。我们首先介绍了一种新的技术，获得感受野向量（RFV），它利用修改后的形式的互信息（“二次互信息”）。我们分析了在呈现短时间（~10秒）复杂视觉场景（自然电影）时RGC的放电模式。我们探索了由视觉输入形成的高维空间，以获得RFV的更小维度的子空间，该子空间给出了关于每个细胞的响应的最多信息。新技术是非常有效和快速的，并且即使每个细胞的尖峰数量有限，也可以导出由自然场景视觉输入形成的新颖类型的RFV。这种方法使我们能够通过计算作为帧数和半径的函数的互信息来估计每种细胞类型的“视觉记忆”和相应的感受野面积。最后，我们根据每种细胞类型的RFV预测了生物学相关功能。RGC类别分析补充了细胞对黑色和白色斑点刺激形式的简单视觉输入的响应结果，以及它们在几个关键生理指标上的分类。因此，RFV导致基于有限数据的生物学作用的预测，并促进来自定义的细胞类型的感觉诱发的尖峰数据的分析。
Sensory stimuli are encoded by diverse kinds of neurons but the identities of the recorded neurons that are studied are often unknown. We explored in detail the firing patterns of eight previously defined genetically-identified retinal ganglion cell (RGC) types from a single transgenic mouse line. We first introduce a new technique of deriving receptive field vectors (RFVs) which utilises a modified form of mutual information (“Quadratic Mutual Information”). We analysed the firing patterns of RGCs during presentation of short duration (~10 second) complex visual scenes (natural movies). We probed the high dimensional space formed by the visual input for a much smaller dimensional subspace of RFVs that give the most information about the response of each cell. The new technique is very efficient and fast and the derivation of novel types of RFVs formed by the natural scene visual input was possible even with limited numbers of spikes per cell. This approach enabled us to estimate the 'visual memory' of each cell type and the corresponding receptive field area by calculating Mutual Information as a function of the number of frames and radius. Finally, we made predictions of biologically relevant functions based on the RFVs of each cell type. RGC class analysis was complemented with results for the cells’ response to simple visual input in the form of black and white spot stimulation, and their classification on several key physiological metrics. Thus RFVs lead to predictions of biological roles based on limited data and facilitate analysis of sensory-evoked spiking data from defined cell types.