Department of Physics, Bar-Ilan University, 52900, Ramat-Gan, Israel.
Gonda Interdisciplinary Brain Research Center, Bar-Ilan University, 52900, Ramat-Gan, Israel.
Sci Rep. 2023 Jan 30;13(1):962. doi: 10.1038/s41598-023-27986-6.
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.
先进的深度学习架构由数十个全连接和卷积隐藏层组成，目前已扩展到数百个，远非生物实现。它们不合理的生物动力学依赖于以非局部方式改变权重，因为输出单元和权重之间的路径数量通常很大，使用反向传播技术。在这里，开发了一种受基于实验的树突树适应启发的 3 层树架构，并将其应用于 CIFAR-10 数据库的离线和在线学习。所提出的架构优于 5 层卷积 LeNet 的可实现成功率。此外，所提出的架构的高度修剪的树反向传播方法，其中单个路径连接输出单元和权重，代表了有效的树突深度学习。
