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Learning in Neuromorphic Systems

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Neuromorphic Computing Principles and Organization

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Abstract

The human brain is regarded as a power-efficient learning machine capable of carrying out complex computations while using only little resources. A sophisticated property that makes energy-efficient computation possible is the distinct sparse communication among many spiking neurons. The primary goal of neuromorphic hardware is to emulate brain-like neural networks to solve real-world problems. However, training on neuromorphic systems is challenging due to the required non-local computations of gradient-based learning algorithms. In Spiking neural networks, there are two fundamental modes: inference and learning. The learning phase, which minimizes a particular cost or loss function, is a complex process of acquiring the parameters to output the correct inference results. On the other hand, the inference computes the output values based on the given input and the network parameters. This chapter presents how learning in neuromorphic computing systems is conducted.

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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). Learning in Neuromorphic Systems. In: Neuromorphic Computing Principles and Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-92525-3_3

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  • DOI: https://doi.org/10.1007/978-3-030-92525-3_3

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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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