从神经连接组学自动发现细胞类型和微电路
Automatic discovery of cell types and microcircuitry from neural connectomics
摘要：
神经连接组学已经开始产生大量的数据，需要新的分析方法来发现生物和计算结构。长期以来，人们一直认为发现神经元类型及其与微电路的关系对于理解神经功能至关重要。在这里，我们开发了一种非参数贝叶斯技术，识别神经元类型和连接组学数据中的微电路模式。它结合了生物学家传统上使用的信息，包括连接性，细胞体位置和突触的空间分布，以原则和概率一致的方式。我们表明，该方法可以恢复视网膜中已知的神经元类型，并能够预测连通性，优于简单的算法。它也可以揭示有趣的结构，在神经系统的C。elegans，并自动发现微处理器的结构。我们的方法从连接组学中提取结构意义，从而实现从这些新兴数据集自动获得解剖学见解的新方法。
英文摘要：
Neural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure. It has long been assumed that discovering neuron types and their relation to microcircuitry is crucial to understanding neural function. Here we developed a nonparametric Bayesian technique that identifies neuron types and microcircuitry patterns in connectomics data. It combines the information traditionally used by biologists, including connectivity, cell body location and the spatial distribution of synapses, in a principled and probabilistically-coherent manner. We show that the approach recovers known neuron types in the retina and enables predictions of connectivity, better than simpler algorithms. It also can reveal interesting structure in the nervous system of C. elegans, and automatically discovers the structure of a microprocessor. Our approach extracts structural meaning from connectomics, enabling new approaches of automatically deriving anatomical insights from these emerging datasets.
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