Learning representations in Bayesian Confidence Propagation neural networks

被引：7

作者：

Ravichandran, Naresh Balaji ^{[1
]}

Lansner, Anders ^{[2
,3
]}

Herman, Pawel ^{[1
]}

机构：

[1] KTH Royal Inst Technol, Computat Brain Sci Lab, Stockholm, Sweden

[2] Stockholm Univ, Computat Brain Sci Lab, Stockholm, Sweden

[3] KTH Royal Inst Technol, Stockholm, Sweden

来源：

2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2020年

关键词：

neural networks; brain-like computing; bio-inspired; unsupervised learning; structural plasticity; ATTRACTOR NETWORK; MODEL;

D O I：

10.1109/ijcnn48605.2020.9207061

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：7

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