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Introduction to Neuromorphic Computing Systems

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Neuromorphic Computing Principles and Organization

Abstract

The term neuromorphic is generally used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement several models of neural systems. The implementation of neuromorphic computing on the hardware level can be realized by various technologies, including spintronic memories, threshold switches, CMOS transistors, and oxide-based memristors. This chapter introduces the neuromorphic computing systems and explores the fundamental concepts underlying this emerging paradigm. We first discuss biological neurons and the dynamics that are abstracted from them to model artificial neurons. Next, we discuss artificial neurons and how they have evolved in their representation of biological neuronal dynamics. Afterward, we discuss implementing these neural networks in terms of neuron models, storage technologies, inter-neuron communication networks, and learning.

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Authors and Affiliations

  1. University of Aizu, Aizu-Wakamatsu, Japan

    Abderazek Ben Abdallah

  2. University of Aizu, Aizu-Wakamatsu, Japan

    Khanh N. Dang

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  1. Abderazek Ben Abdallah
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  2. Khanh N. Dang
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Correspondence to Abderazek Ben Abdallah or Khanh N. Dang .

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Ben Abdallah, A., N. Dang, K. (2022). Introduction to Neuromorphic Computing Systems. In: Neuromorphic Computing Principles and Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-92525-3_1

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  • DOI: https://doi.org/10.1007/978-3-030-92525-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92524-6

  • Online ISBN: 978-3-030-92525-3

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