Convolutional Spiking Neural Network for Image Classification

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.

@article{kiselev2025_2505.08514,
  title={ Convolutional Spiking Neural Network for Image Classification },
  author={ Mikhail Kiselev and Andrey Lavrentyev },
  journal={arXiv preprint arXiv:2505.08514},
  year={ 2025 }
}