Deep neural networks have become increasingly large and sparse, allowing for the storage of large-scale neural networks with decreased costs of storage and computation. Storage of a neural network with as many connections as the human brain is possible with current versions of the high-performance Apache Accumulo database and the Distributed Dimensional Data Model (D4M) software. Neural networks of such large scale may be of particular interest to scientists within the human brain Connectome community. To aid in research and understanding of artificial neural networks that parallel existing neural networks like the brain, a naming schema can be developed to label groups of neurons in the artificial network that parallel those in the brain. Groups of artificial neurons are able to be specifically labeled in small regions for future study.
翻译:深神经网络变得越来越庞大和稀少,可以储存大型神经网络,而储存和计算成本降低; 储存一个神经网络,与人类大脑有尽可能多的连接,使用高性能的阿帕奇-阿库穆洛数据库和分布多维数据模型(D4M)软件的现有版本是可能的; 大型神经网络对于人类大脑连接网社区的科学家来说可能特别感兴趣; 为了帮助研究和了解与像大脑这样的现有神经网络平行的人工神经网络,可以开发出一个命名系统,在人造网络中标出与大脑相平行的神经组。 人造神经组可以在小区域具体标出未来研究的标签。