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

Source: [Classifying Images with CoLaNET Spiking Neural Network -- the MNIST Example](<https://www.sota2.com/research/paper/68fa74cd1e209131cea63f4f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:18:28.201Z

Authors: Mikhail Kiselev

Published: 2024-09-12T08:29:55.000Z

## Abstract

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).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2409.07833>)

[PDF](<https://arxiv.org/pdf/2409.07833.pdf>)