Emerging applications of Deep Learning and Spiking ANN

Lazaros S. Iliadis; Chrisina Jayne

doi:10.1007/s00521-020-05443-z

Emerging applications of Deep Learning and Spiking ANN

Lazaros S. Iliadis
, Chrisina Jayne

Research output: Contribution to journal › Editorial › peer-review

Abstract

This is the Special Issue on “Emerging applications of Deep Learning (DL) and Spiking Artificial Neural Networks” of the Springer Neural Computing and Applications Journal. It includes seventeen high-quality scientific research papers, presenting innovative research, falling in the above areas. The accepted papers were selected from a large pool of submissions after a peer review process, based on their level of novelty and quality.

Original language	English
Pages (from-to)	17119-17124
Number of pages	6
Journal	Neural Computing and Applications
Volume	32
Issue number	23
Early online date	29 Oct 2020
DOIs	https://doi.org/10.1007/s00521-020-05443-z
Publication status	Published - 1 Dec 2020

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Copyright 2020 Elsevier B.V., All rights reserved.

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title = "Emerging applications of Deep Learning and Spiking ANN",

abstract = "This is the Special Issue on “Emerging applications of Deep Learning (DL) and Spiking Artificial Neural Networks” of the Springer Neural Computing and Applications Journal. It includes seventeen high-quality scientific research papers, presenting innovative research, falling in the above areas. The accepted papers were selected from a large pool of submissions after a peer review process, based on their level of novelty and quality.",

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AB - This is the Special Issue on “Emerging applications of Deep Learning (DL) and Spiking Artificial Neural Networks” of the Springer Neural Computing and Applications Journal. It includes seventeen high-quality scientific research papers, presenting innovative research, falling in the above areas. The accepted papers were selected from a large pool of submissions after a peer review process, based on their level of novelty and quality.

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ER -

Emerging applications of Deep Learning and Spiking ANN

Abstract

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