Construction and Application of a Hybrid Spike Neural Network Image Recognition Algorithm Model Based on Transformer Architecture

With the rapid development of deep learning technology, spiking neural networks (SNNs) and transformer models have made significant progress in computer vision. This article proposes an image recognition algorithm model that combines SNN and transformer networks and validates it using the Modified National Institute of Standards and Technology (MNIST), Fashion-MNIST, and CIFAR-10 datasets. During preprocessing, Poisson encoding transforms pixel intensities into spike sequences. In the hybrid network architecture constructed, the SNN is embedded into the transformer encoder, and its output features are transformed before being passed to the transformer decoder. The spike neural network is constructed with LIF (leaky integrate-and-fire) neurons to build the network layer, and feature learning and extraction are carried out using the spike-timing-dependent plasticity (STDP) learning rule. The decoder recognizes the features extracted by the encoder, and the cross-entropy loss function is used to compute the difference between the output results and the true labels. During the training phase, the encoder and decoder parameters are updated simultaneously through backpropagation. The model is evaluated using independent test sets and validation sets. The experimental results show that after 100 iterations, the hybrid model using only the LIF neuron network achieves validation accuracies of 0.9803, 0.9262, and 0.7217 on the MNIST, Fashion-MNIST, and CIFAR-10 datasets, respectively. Further research reveals that incorporating the STDP learning rule improves the model’s validation accuracy to 0.9977, 0.9315, and 0.7461, demonstrating that this approach can enhance recognition accuracy to some extent, offering new insights for further research in image recognition.