Advanced Computing and Related Applications Leveraging Brain-inspired Spiking Neural Networks
Lyuyang Sima, Joseph Bucukovski, Erwan Carlson, Nicole L. Yien

TL;DR
This paper reviews the current state of spiking neural networks, including models, topologies, learning algorithms, and neuromorphic hardware, highlighting their potential in brain-inspired AI and real-time processing.
Contribution
It provides a comprehensive summary of neuronal models, network topologies, learning algorithms, and neuromorphic chips, offering guidance for newcomers to the field.
Findings
Analysis of five neuronal models and their strengths and weaknesses.
Summary of unsupervised and supervised learning algorithms for SNNs.
Review of current neuromorphic chip research worldwide.
Abstract
In the rapid evolution of next-generation brain-inspired artificial intelligence and increasingly sophisticated electromagnetic environment, the most bionic characteristics and anti-interference performance of spiking neural networks show great potential in terms of computational speed, real-time information processing, and spatio-temporal information processing. Data processing. Spiking neural network is one of the cores of brain-like artificial intelligence, which realizes brain-like computing by simulating the structure and information transfer mode of biological neural networks. This paper summarizes the strengths, weaknesses and applicability of five neuronal models and analyzes the characteristics of five network topologies; then reviews the spiking neural network algorithms and summarizes the unsupervised learning algorithms based on synaptic plasticity rules and four types of…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Neural Networks and Applications · Robotics and Automated Systems
