Analog-Digital Approach in Human Brain Modeling

Alexander Bogdanov, Alexander Degtyarev, Dmitriy Guschanskiy, Kirill Lysov, Nataliya Ananieva, Nataliya Zalutskaya, Nikolay Neznanov

Research outputpeer-review

1 Citation (Scopus)

Abstract

Many companies and institutions in their attempts construct decision-making system, face a bottleneck in performance of their systems. Training neural networks can take from several days to several weeks. The traditional approach suggests modification of modern systems and microcircuits as long as their performance reaches a permissible limit. A different approach, unconventional, looks for opportunities in computing inspired by the human brain, neuromorphic computing. The idea was proposed by the engineer Carver Mead in the 80s and suggests combining artificial neural networks with specialized microcircuits. The architecture of the microchip needs to reproduce the mechanisms of the human brain and to be a kind of hardware support for neural networks. Last decade is characterized by a sharp growth of interest in neuromorphic computing, human brain modeling and peculiarities of how it works during making decisions. This is evidenced by the launch of a large-scale research programs like DARPA SyNAPSE (USA) and
Original languageEnglish
Title of host publicationProceeding CCGrid '17 Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
Place of PublicationNJ, USA
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages807-812
ISBN (Print)978-1-5090-6610-0
DOIs
Publication statusPublished - 2017

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