Analog-Digital Approach in Human Brain Modeling

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

Research output

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

Fingerprint

Brain
Neural networks
Decision making
Hardware
Engineers
Industry

Cite this

Bogdanov, A., Degtyarev, A., Guschanskiy, D., Lysov, K., Ananieva, N., Zalutskaya, N., & Neznanov, N. (2017). Analog-Digital Approach in Human Brain Modeling. In Proceeding CCGrid '17 Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (pp. 807-812). NJ, USA: Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCGRID.2017.91, https://doi.org/10.1109/CCGRID.2017.91
Bogdanov, Alexander ; Degtyarev, Alexander ; Guschanskiy, Dmitriy ; Lysov, Kirill ; Ananieva, Nataliya ; Zalutskaya, Nataliya ; Neznanov, Nikolay. / Analog-Digital Approach in Human Brain Modeling. Proceeding CCGrid '17 Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. NJ, USA : Institute of Electrical and Electronics Engineers Inc., 2017. pp. 807-812
@inproceedings{45469ed6541b4e0abcbf1b50a0230d38,
title = "Analog-Digital Approach in Human Brain Modeling",
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",
author = "Alexander Bogdanov and Alexander Degtyarev and Dmitriy Guschanskiy and Kirill Lysov and Nataliya Ananieva and Nataliya Zalutskaya and Nikolay Neznanov",
year = "2017",
doi = "10.1109/CCGRID.2017.91",
language = "English",
isbn = "978-1-5090-6610-0",
pages = "807--812",
booktitle = "Proceeding CCGrid '17 Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

Bogdanov, A, Degtyarev, A, Guschanskiy, D, Lysov, K, Ananieva, N, Zalutskaya, N & Neznanov, N 2017, Analog-Digital Approach in Human Brain Modeling. in Proceeding CCGrid '17 Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. Institute of Electrical and Electronics Engineers Inc., NJ, USA, pp. 807-812. https://doi.org/10.1109/CCGRID.2017.91, https://doi.org/10.1109/CCGRID.2017.91

Analog-Digital Approach in Human Brain Modeling. / Bogdanov, Alexander; Degtyarev, Alexander; Guschanskiy, Dmitriy; Lysov, Kirill; Ananieva, Nataliya; Zalutskaya, Nataliya; Neznanov, Nikolay.

Proceeding CCGrid '17 Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. NJ, USA : Institute of Electrical and Electronics Engineers Inc., 2017. p. 807-812.

Research output

TY - GEN

T1 - Analog-Digital Approach in Human Brain Modeling

AU - Bogdanov, Alexander

AU - Degtyarev, Alexander

AU - Guschanskiy, Dmitriy

AU - Lysov, Kirill

AU - Ananieva, Nataliya

AU - Zalutskaya, Nataliya

AU - Neznanov, Nikolay

PY - 2017

Y1 - 2017

N2 - 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

AB - 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

U2 - 10.1109/CCGRID.2017.91

DO - 10.1109/CCGRID.2017.91

M3 - Conference contribution

SN - 978-1-5090-6610-0

SP - 807

EP - 812

BT - Proceeding CCGrid '17 Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

PB - Institute of Electrical and Electronics Engineers Inc.

CY - NJ, USA

ER -

Bogdanov A, Degtyarev A, Guschanskiy D, Lysov K, Ananieva N, Zalutskaya N et al. Analog-Digital Approach in Human Brain Modeling. In Proceeding CCGrid '17 Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. NJ, USA: Institute of Electrical and Electronics Engineers Inc. 2017. p. 807-812 https://doi.org/10.1109/CCGRID.2017.91, https://doi.org/10.1109/CCGRID.2017.91