# Classifying Images with CoLaNET Spiking Neural Network -- the MNIST Example

arXiv: https://arxiv.org/abs/2409.07833 · HTML: https://mlsanity.com/paper/2409.07833/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2024.

## Built on by (most cited first, 3 of 3)

- [Continual Learning with Columnar Spiking Neural Networks](https://mlsanity.com/paper/2506.17169/) (2025)
- [Spiffy: Efficient Implementation of CoLaNET for Raspberry Pi](https://mlsanity.com/paper/2506.18306/) (2025)
- [Hybrid ANN-SNN Pipeline with Local Plasticity](https://mlsanity.com/paper/2606.20151/) (2026)

## Abstract (arXiv)

In the present paper, it is shown how the columnar/layered CoLaNET spiking neural network (SNN) architecture can be used in supervised learning image classification tasks. Image pixel brightness is coded by the spike count during image presentation period. Image class label is indicated by activity of special SNN input nodes (one node per class). The CoLaNET classification accuracy is evaluated on the MNIST benchmark. It is demonstrated that CoLaNET is almost as accurate as the most advanced machine learning algorithms (not using convolutional approach).