Advanced deep learning architectures consist of tens of fully connected and convolutional hidden layers, currently extended to hundreds, are far from their biological realization. Their implausible biological dynamics relies 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, a 3-layer tree architecture inspired by experimental-based dendritic tree adaptations is developed and applied to the offline and online learning of the CIFAR-10 database. The proposed architecture outperforms the achievable success rates of the 5-layer convolutional LeNet. Moreover, the highly pruned tree backpropagation approach of the proposed architecture, 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蔽层组成,目前扩大到数百个,远远没有在生物学上实现,其不可信的生物动态依赖于以非局部方式改变重量,因为一个产出单位和一个重量之间的路线数量通常很大,使用回压技术。在这里,开发了一个由实验性成形的三层树结构,并应用于CIFAR-10数据库的离线和在线学习。拟议的结构优于5层相向LeNet的可实现成功率。此外,一个单一路径连接一个输出单位和一个重量,高度修剪的树背对流法代表一种高效的进化深层学习。