Comparing performance of neural network frameworks for image recognition: A review

Abstract

Spiking Neural Networks (SNNs) are increasingly recognized as a promising approach to simulating the biological behavior of neurons. This study conducted an in-depth performance comparison of different SNN models with supervised learning algorithms, particularly in the field of image recognition. Using datasets like MNIST, CIFAR-10, and ImageNet, the research analyzed the performance and accuracy of SNNs compared to traditional Artificial Neural Networks (ANNs). This research contributes to understanding SNNs' performance in supervised learning environments and offers insights into the optimization of SNN architectures, thus influencing future research and the development of next-generation neural network designs. Based on this comparative study, it was found that while SNNs perform admirably with smaller, less complex datasets such as MNIST and CIFAR, they encounter difficulties with larger, more complex datasets like ImageNet. Despite these challenges, advances in conversion techniques have led to models that more closely simulate the behavior of biological neurons. The study identified significant future work areas, including addressing issues of local learning methods, the limited scale of SNNs, hardware implementation difficulties, a lack of substantial benchmark datasets, and limited support for complex computations.