A Survey on Spiking Neural Network Foundation and Recent Progress

Authors: Nhan Trong Luu, Duong Trung Luu, Pham Ngoc Nam, Truong Cong Thang
Published: 2026
Journal: IEEE Access, Vol 14
DOI: 10.1109/ACCESS.2026.3685666

Abstract

Over the past decade, deep neural networks (DNNs) have achieved remarkable success across numerous fields. Despite this progress, DNNs remain highly demanding in terms of computational power, energy consumption, and data requirements. As the demand for autonomous systems such as self-driving cars, drones, collaborative robots, etc. continues to rise, the deployment of DNNs in these real-world scenarios has gained significant attention. However, these applications require energy-efficient and computationally lightweight models to ensure real-time performance under limited power resources. A promising alternative has emerged in the form of biologically inspired spiking neural networks (SNNs), which offer a more energy-efficient and temporally dynamic computing paradigm. By mimicking the behavior of biological neurons, SNNs provide a bridge between neuroscience and machine learning, leveraging neural sparsity and temporal coding mechanisms inherent to biological systems. In this work, we make the following contributions: provide a comprehensive overview of biological neuron theories; summarize existing spike-based neuron models from neuroscience; describe synaptic modeling approaches; review conventional artificial neural networks; outline several methodologies for preprocessing, training and conversion approaches for spike-based models; explore SNN applications in various fields; discuss major frameworks supporting SNN implementation.