Convolutional Spiking Neural Network for Image Classification

Fuente: arXiv
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Autori principali: Kiselev, Mikhail, Lavrentyev, Andrey
Natura: Preprint
Pubblicazione: 2025
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author Kiselev, Mikhail
Lavrentyev, Andrey
author_facet Kiselev, Mikhail
Lavrentyev, Andrey
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2505_08514
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Convolutional Spiking Neural Network for Image Classification
Kiselev, Mikhail
Lavrentyev, Andrey
Neural and Evolutionary Computing
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.
title Convolutional Spiking Neural Network for Image Classification
topic Neural and Evolutionary Computing
url https://arxiv.org/abs/2505.08514