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A Hardware Implementation of SNN-Based Spatio-Temporal Memory Model

Simulating human brain with hardware has been an attractive project for many years, since memory is one of the fundamental functions of our brains. Several memory models have been proposed up to now in order to unveil how the memory is organized in the brain. In this paper, we adopt spatio-temporal...

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Autores principales: Liu, Kefei, Cui, Xiaoxin, Zhong, Yi, Kuang, Yisong, Wang, Yuan, Tang, Huajin, Huang, Ru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697024/
https://www.ncbi.nlm.nih.gov/pubmed/31447641
http://dx.doi.org/10.3389/fnins.2019.00835
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author Liu, Kefei
Cui, Xiaoxin
Zhong, Yi
Kuang, Yisong
Wang, Yuan
Tang, Huajin
Huang, Ru
author_facet Liu, Kefei
Cui, Xiaoxin
Zhong, Yi
Kuang, Yisong
Wang, Yuan
Tang, Huajin
Huang, Ru
author_sort Liu, Kefei
collection PubMed
description Simulating human brain with hardware has been an attractive project for many years, since memory is one of the fundamental functions of our brains. Several memory models have been proposed up to now in order to unveil how the memory is organized in the brain. In this paper, we adopt spatio-temporal memory (STM) model, in which both associative memory and episodic memory are analyzed and emulated, as the reference of our hardware network architecture. Furthermore, some reasonable adaptations are carried out for the hardware implementation. We finally implement this memory model on FPGA, and additional experiments are performed to fine tune the parameters of our network deployed on FPGA.
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spelling pubmed-66970242019-08-23 A Hardware Implementation of SNN-Based Spatio-Temporal Memory Model Liu, Kefei Cui, Xiaoxin Zhong, Yi Kuang, Yisong Wang, Yuan Tang, Huajin Huang, Ru Front Neurosci Neuroscience Simulating human brain with hardware has been an attractive project for many years, since memory is one of the fundamental functions of our brains. Several memory models have been proposed up to now in order to unveil how the memory is organized in the brain. In this paper, we adopt spatio-temporal memory (STM) model, in which both associative memory and episodic memory are analyzed and emulated, as the reference of our hardware network architecture. Furthermore, some reasonable adaptations are carried out for the hardware implementation. We finally implement this memory model on FPGA, and additional experiments are performed to fine tune the parameters of our network deployed on FPGA. Frontiers Media S.A. 2019-08-09 /pmc/articles/PMC6697024/ /pubmed/31447641 http://dx.doi.org/10.3389/fnins.2019.00835 Text en Copyright © 2019 Liu, Cui, Zhong, Kuang, Wang, Tang and Huang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Liu, Kefei
Cui, Xiaoxin
Zhong, Yi
Kuang, Yisong
Wang, Yuan
Tang, Huajin
Huang, Ru
A Hardware Implementation of SNN-Based Spatio-Temporal Memory Model
title A Hardware Implementation of SNN-Based Spatio-Temporal Memory Model
title_full A Hardware Implementation of SNN-Based Spatio-Temporal Memory Model
title_fullStr A Hardware Implementation of SNN-Based Spatio-Temporal Memory Model
title_full_unstemmed A Hardware Implementation of SNN-Based Spatio-Temporal Memory Model
title_short A Hardware Implementation of SNN-Based Spatio-Temporal Memory Model
title_sort hardware implementation of snn-based spatio-temporal memory model
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697024/
https://www.ncbi.nlm.nih.gov/pubmed/31447641
http://dx.doi.org/10.3389/fnins.2019.00835
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