Convolutional Spiking Neural Network for Image Classification
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916735149408256 |
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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 |