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2019 | OriginalPaper | Buchkapitel

13. Deep Learning and Modelling of Audio-, Visual-, and Multimodal Audio-Visual Data in Brain-Inspired SNN

verfasst von : Nikola K. Kasabov

Erschienen in: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Verlag: Springer Berlin Heidelberg

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Abstract

This chapter presents methods for audio-, visual- and for the integrated audio and visual information processing using brain-inspired SNN architectures such as NeuCube. Case studies are presented for short musical pieces recognition, fast moving object recognition, age-invariant face identification, moving digits recognition and other.

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Titel: Deep Learning and Modelling of Audio-, Visual-, and Multimodal Audio-Visual Data in Brain-Inspired SNN
verfasst von: Nikola K. Kasabov
Verlag: Springer Berlin Heidelberg
Buch: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence
Print ISBN: 978-3-662-57713-4

Electronic ISBN: 978-3-662-57715-8

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-662-57715-8_13

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