# Convolutional Spiking Neural Network for Image Classification

Source: [Convolutional Spiking Neural Network for Image Classification](<https://www.sota2.com/research/paper/68fa788a1e209131cea78f58>)

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

Data updated: 2025-10-26T04:41:15.331Z

Authors: Mikhail Kiselev, Andrey Lavrentyev

Published: 2025-05-13T12:47:13.000Z

## Abstract

We consider an implementation of convolutional architecture in a spiking neural network (SNN) used to classify images. As in the traditional neural network, the convolutional layers form informational "features" used as predictors in the SNN-based classifier with CoLaNET architecture. Since weight sharing contradicts the synaptic plasticity locality principle, the convolutional weights are fixed in our approach. We describe a methodology for their determination from a representative set of images from the same domain as the classified ones. We illustrate and test our approach on a classification task from the NEOVISION2 benchmark.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2505.08514>)

[PDF](<https://arxiv.org/pdf/2505.08514.pdf>)