Validation of spiking neural networks using resistive-switching synaptic device with spike-rate-dependent plasticity

Suhyun Bang, Min Hye Oh, Min Hwi Kim, Tae Hyeon Kim, Dong Keun Lee, Yeon Joon Choi, Chae Soo Kim, Kyungho Hong, Seongjae Cho, Sungjun Kim, Byung Gook Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this work, we have developed a spiking neural network (SNN) using gradual resistive-switching random-access memory (RRAM) synaptic device. The fabricated RRAM devices demonstrated the characteristics of gradually changing conductance with voltage pulses under both positive and negative polarities, which is suitable for imitating the potentiation and depression functions of a biological synapse by an electron device. Featuring the gradual switching characteristics, spike-rate-dependent plasticity (SRDP) inspired by Bienenstock, Cooper, and Munro (BCM) learning rule was confirmed and modeled for synaptic modification in the SNN. Then, the supervised learning of MNIST patterns was performed on the simulated SNNs, by which it has been validated that the proposed resistive-switching synaptic device and SRDP synaptic modification rule can adjust weights accurately in cooperation without necessitating the conventional calculation-based learning scheme in the artificial neural networks (ANNs), such as error backpropagation.

Original languageEnglish
Title of host publication2020 International Conference on Electronics, Information, and Communication, ICEIC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728162898
DOIs
StatePublished - Jan 2020
Event2020 International Conference on Electronics, Information, and Communication, ICEIC 2020 - Barcelona, Spain
Duration: 19 Jan 202022 Jan 2020

Publication series

Name2020 International Conference on Electronics, Information, and Communication, ICEIC 2020

Conference

Conference2020 International Conference on Electronics, Information, and Communication, ICEIC 2020
Country/TerritorySpain
CityBarcelona
Period19/01/2022/01/20

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported by the National Research Foundation (NRF) Grant funded by the Ministry of Science and ICT of Korea (MSIT) (2018R1A2A1A05023517 and 2016M3A7B4910348) and by the Brain Korea 21 Plus Program in 2019.

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Resistive-switching random-access memory
  • Spike-rate-dependent plasticity
  • Spiking neural network
  • Synaptic device

Fingerprint

Dive into the research topics of 'Validation of spiking neural networks using resistive-switching synaptic device with spike-rate-dependent plasticity'. Together they form a unique fingerprint.

Cite this