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.Comment: 20 pages, 4 figures, 1 table (improved figures resolution