Controllable digital resistive switching for artificial synapses and pavlovian learning algorithm-Reference-Cited by-同舟云学术

Controllable digital resistive switching for artificial synapses and pavlovian learning algorithm

Published:2019 Issue:33 Volume:11 Page:15596-15604
ISSN:2040-3364
Container-title:Nanoscale
language:en
Short-container-title:Nanoscale

Author:

Kumar Mohit¹²³⁴⁵,Abbas Sohail¹²³⁴⁵^ORCID,Lee Jung-Ho⁶⁷⁸⁹^ORCID,Kim Joondong¹²³⁴⁵^ORCID

Affiliation:

1. Photoelectric and Energy Device Application Lab (PEDAL)

2. Multidisciplinary Core Institute for Future Energies (MCIFE)

3. Incheon

4. Republic of Korea

5. Department of Electrical Engineering

6. Department of Materials and Chemical Engineering

7. Hanyang University

8. Ansan

9. Korea

Abstract

Synaptic response has been enhanced by 340 times by geometrical modulation of a ZnO-based memristor. The device showed a variety of comprehensive synaptic functions, including the Pavlovian associative learning process in the human brain.

Funder

National Research Foundation of Korea

Ministry of Trade, Industry and Energy

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2019/NR/C9NR02027F

Reference46 articles.

1. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing

2. Flexible Artificial Synaptic Devices Based on Collagen from Fish Protein with Spike-Timing-Dependent Plasticity

3. Memristor with Ag-Cluster-Doped TiO2Films as Artificial Synapse for Neuroinspired Computing

4. Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability

5. Restickable Oxide Neuromorphic Transistors with Spike-Timing-Dependent Plasticity and Pavlovian Associative Learning Activities

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