Non-parametric physiological classification of retinal ganglion cells in the mouse retina
Non-parametric physiological classification of retinal ganglion cells in the mouse retina
复制标题
小鼠视网膜视网膜神经节细胞的非参数生理学分类
DOI：
10.1101/407635
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Jouty J
中科院分区：
文献类型：
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作者：
Jouty J
视网膜神经节细胞，视网膜的唯一输出神经元，表现出惊人的多样性。最近的一项研究报告了老鼠视网膜中超过30种不同类型的视网膜，这表明大脑对视觉信息的处理是高度并行的。高密度多电极阵列的出现，现在可以记录来自单个视网膜的数百到数千个神经元。在这里，我们描述了一种方法来自动分类大规模视网膜记录使用一个简单的刺激范式和尖峰列车距离测量作为聚类度量。我们使用合成尖峰序列来评估我们的方法，并证明主要已知的细胞类型是在小鼠视网膜的高密度记录会话中识别的，大约有1000个视网膜神经节细胞。不同视网膜之间的比较揭示了制剂之间的实质性差异，提示在视网膜之间汇集数据应谨慎处理。作为一种无参数的方法，我们的方法广泛适用于所有感觉模式的细胞生理分类。
Retinal ganglion cells, the sole output neurons of the retina, exhibit surprising diversity. A recent study reported over 30 distinct types in the mouse retina, indicating that the processing of visual information is highly parallelised in the brain. The advent of high density multi-electrode arrays now enables recording from many hundreds to thousands of neurons from a single retina. Here we describe a method for the automatic classification of large-scale retinal recordings using a simple stimulus paradigm and a spike train distance measure as a clustering metric. We evaluate our approach using synthetic spike trains, and demonstrate that major known cell types are identified in high-density recording sessions from the mouse retina with around 1,000 retinal ganglion cells. A comparison across different retinas reveals substantial variability between preparations, suggesting pooling data across retinas should be approached with caution. As a parameter-free method, our approach is broadly applicable for cellular physiological classification in all sensory modalities.