# Spiking neural networks with Hebbian plasticity for unsupervised representation learning

arXiv: https://arxiv.org/abs/2305.03866 · HTML: https://mlsanity.com/paper/2305.03866/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 5 of 5)

- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule](https://mlsanity.com/paper/1812.06574/) (2018)
- [Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks](https://mlsanity.com/paper/1804.00227/) (2018)
- [An Unsupervised STDP-based Spiking Neural Network Inspired By Biologically Plausible Learning Rules and Connections](https://mlsanity.com/paper/2207.02727/) (2022)
- [Learning representations in Bayesian Confidence Propagation neural networks](https://mlsanity.com/paper/2003.12415/) (2020)

## Abstract (arXiv)

We introduce a novel spiking neural network model for learning distributed internal representations from data in an unsupervised procedure. We achieved this by transforming the non-spiking feedforward Bayesian Confidence Propagation Neural Network (BCPNN) model, employing an online correlation-based Hebbian-Bayesian learning and rewiring mechanism, shown previously to perform representation learning, into a spiking neural network with Poisson statistics and low firing rate comparable to in vivo cortical pyramidal neurons. We evaluated the representations learned by our spiking model using a linear classifier and show performance close to the non-spiking BCPNN, and competitive with other Hebbian-based spiking networks when trained on MNIST and F-MNIST machine learning benchmarks.