Transcriptome in brain plays a crucial role in understanding the cortical organization and the development of brain structure and function. Two challenges, incomplete data and high dimensionality of transcriptome, remain unsolved. Here, we present a novel training scheme that successfully adapts the U-net architecture to the problem of volume recovery. By analogy to denoising autoencoder, we hide a portion of each training sample so that the network can learn to recover missing voxels from context. Then on the completed volumes, we show that Restricted Boltzmann Machines (RBMs) can be used to infer co-occurrences among voxels, providing foundations for dividing the cortex into discrete subregions. As we stack multiple RBMs to form a deep belief network (DBN), we progressively map the high-dimensional raw input into abstract representations and create a hierarchy of transcriptome architecture. A coarse to fine organization emerges from the network layers. This organization incidentally corresponds to the anatomical structures, suggesting a close link between structures and the genetic underpinnings. Thus, we demonstrate a new way of learning transcriptome-based hierarchical organization using RBM and DBN.

中文翻译：

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用于原位杂交网格完成和聚类的深度神经网络。


大脑转录组在理解皮质组织以及大脑结构和功能的发展中起着至关重要的作用。数据不完整和转录组的高维性这两个挑战仍未解决。在这里，我们提出了一种新颖的训练方案，成功地将 U-net 架构应用于卷恢复问题。通过类比去噪自动编码器，我们隐藏每个训练样本的一部分，以便网络可以学习从上下文中恢复丢失的体素。然后，在完成的卷上，我们表明受限玻尔兹曼机（RBM）可用于推断体素之间的共现，为将皮层划分为离散子区域提供基础。当我们堆叠多个 RBM 形成深度信念网络 (DBN) 时，我们逐渐将高维原始输入映射为抽象表示，并创建转录组架构的层次结构。网络层中出现从粗到细的组织。这种组织恰好对应于解剖结构，表明结构与遗传基础之间存在密切联系。因此，我们展示了一种使用 RBM 和 DBN 学习基于转录组的分层组织的新方法。