Learning representations in Bayesian Confidence Propagation neural networks
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Mar 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 4; 2022: 1; 2023: 1.
6 papers.
- 2021StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAscited by 5
- 2021Using brain inspired principles to unsupervisedly learn good representations for visual pattern recognitioncited by 1
- 2021Semi-supervised learning with Bayesian Confidence Propagation Neural Networkcited by 1
- 2021Higgs Boson Classification: Brain-inspired BCPNN Learning with StreamBraincited by 0
- 2022Multi-level Data Representation For Training Deep Helmholtz Machinescited by 0
- 2023Spiking neural networks with Hebbian plasticity for unsupervised representation learningcited by 0
- 2023Spiking neural networks with Hebbian plasticity for unsupervised representation learning5 May 2023
- 2022Multi-level Data Representation For Training Deep Helmholtz Machines26 Oct 2022
- 2021Higgs Boson Classification: Brain-inspired BCPNN Learning with StreamBrain14 Jul 2021
- 2021Semi-supervised learning with Bayesian Confidence Propagation Neural Network29 Jun 2021
- 2021StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs9 Jun 2021
- 2021Using brain inspired principles to unsupervisedly learn good representations for visual pattern recognition30 Apr 2021