Unsupervised learning of hierarchical representations has been one of the most vibrant research directions in deep learning during recent years. In this work we study biologically inspired unsupervised strategies in neural networks based on local Hebbian learning. We propose new mechanisms to extend the Bayesian Confidence Propagating Neural Network (BCPNN) architecture, and demonstrate their capability for unsupervised learning of salient hidden representations when tested on the MNIST dataset.

中文翻译：

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贝叶斯置信传播神经网络中的学习表示

近年来，分层表示的无监督学习一直是深度学习中最活跃的研究方向之一。在这项工作中，我们研究了基于局部赫布学习的神经网络中受生物学启发的无监督策略。我们提出了扩展贝叶斯置信度传播神经网络 (BCPNN) 架构的新机制，并展示了它们在 MNIST 数据集上进行测试时对显着隐藏表示进行无监督学习的能力。