# Brain-like approaches to unsupervised learning of hidden representations -- a comparative study

arXiv: https://arxiv.org/abs/2005.03476 · HTML: https://mlsanity.com/paper/2005.03476/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 4 of 4)

- [Activation Learning by Local Competitions](https://mlsanity.com/paper/2209.13400/) (2022)
- [StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs](https://mlsanity.com/paper/2106.05373/) (2021)
- [Semi-supervised learning with Bayesian Confidence Propagation Neural Network](https://mlsanity.com/paper/2106.15546/) (2021)
- [Embedded FPGA Acceleration of Brain-Like Neural Networks: Online Learning to Scalable Inference](https://mlsanity.com/paper/2506.18530/) (2025)

## Abstract (arXiv)

Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years. In this work we study the brain-like Bayesian Confidence Propagating Neural Network (BCPNN) model, recently extended to extract sparse distributed high-dimensional representations. The usefulness and class-dependent separability of the hidden representations when trained on MNIST and Fashion-MNIST datasets is studied using an external linear classifier and compared with other unsupervised learning methods that include restricted Boltzmann machines and autoencoders.