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Spike-based neuromorphic computing: An overview from bio-inspiration to hardware architectures and learning mechanisms
Journal article   Open access   Peer reviewed

Spike-based neuromorphic computing: An overview from bio-inspiration to hardware architectures and learning mechanisms

Anteneh Gebregiorgis, Amirreza Yousefzadeh, Sherif Eissa, Muhammad Ali Siddiqi, Charlotte Frenkel, Friedemann Zenke, Sander Bohte, Abdulqader Nael Mahmoud, Anup Das, Said Hamdioui, …
Microprocessors and microsystems, Forthcoming
2026
DOI: https://doi.org/10.1016/j.micpro.2025.105240
url
https://doi.org/10.1016/j.micpro.2025.105240View
Published, Version of Record (VoR) Open

Abstract

Artificial intelligence Computing-in-memory Emerging technologies Hardware architectures Learning mechanisms Neuromorphic computing Spiking neural networks
The endeavor to emulate the extraordinary efficiency and adaptability inherent in the human brain via spike-based neuromorphic computing presents significant potential across a diverse array of applications. The attainment of this objective necessitates the translation of biological principles into artificial systems, a task that continues to pose a complex challenge requiring a profound comprehension of the mechanisms by which neural systems produce robust computational outcomes. This tutorial paper provides a comprehensive overview of the foundational concepts and emerging design trends in spike-based neuromorphic computing, covering advances from materials and circuits to hardware architectures and learning mechanisms. It begins with an examination of key aspects of brain biology and their influence on neuromorphic design, followed by a brief discussion of biologically plausible neuron and synapse models. The paper then defines the core principles and defining attributes of neuromorphic computing, highlighting the trade-offs and design choices underlying current implementations. Building on these foundations, it explores the critical properties of neuromorphic systems, surveys a variety of learning algorithms, and reviews hardware-level realizations of bioinspired neurons and synapses. Subsequent sections discuss state-of-the-art spiking neural network architectures, mapping and compilation strategies, and representative application domains. By providing this end-to-end perspective, the article aims to guide the development of future neuromorphic systems that more closely emulate brain efficiency, scalability, and resilience.

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