Advanced deep learning architectures consist of tens of fully connected and convolutional hidden layers, which are already extended to hundreds, and are far from their biological realization. Their implausible biological dynamics is based on changing a weight in a non-local manner, as the number of routes between an output unit and a weight is typically large, using the backpropagation technique. Here, offline and online CIFAR-10 database learning on 3-layer tree architectures, inspired by experimental-based dendritic tree adaptations, outperforms the achievable success rates of the 5-layer convolutional LeNet. Its highly pruning tree backpropagation procedure, where a single route connects an output unit and a weight, represents an efficient dendritic deep learning.

翻译：先进的深层学习结构由数十个完全相连和进化的隐藏层组成,这些结构已经扩展到数百个,而且远未在生物学上实现。 其不可信的生物动态的基础是以非本地方式改变重量,因为一个输出单位与一个重量之间的路径通常很大,使用反向透析技术。 这里,离线和在线CIFAR-10数据库学习三层树结构,在实验性的登面树改造的启发下,超过了五层相向 LeNet的可实现成功率。 其高度修剪的树背对流程序, 将一个输出单位和一个重量连接在一起, 是一种高效的斜面深层学习。