Tuning Bienenstock–Cooper–Munro learning rules in a two-terminal memristor for neuromorphic computing

Li, Zeyang; Liu, Peilin; Yang, Guanghong; Jia, Caihong; Zhang, Weifeng

doi:10.1039/d3cp01134h

Phys. Chem. Chem. Phys.

2023

DOI: 10.1039/d3cp01134h

|View full text |Cite

Tuning Bienenstock–Cooper–Munro learning rules in a two-terminal memristor for neuromorphic computing

Zeyang Li

Peilin Liu

Guanghong Yang

et al.

Abstract: The non-monotonous BCM learning rule with the enhanced depression effect is achieved by applying presynaptic pulses and changing series resistance.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

BCM‐Inspired Synapses Constructed with Barrier‐Modulated Coupling Junctions for Enhancing Speech Recognition

Cai,

Liu,

Wang

et al. 2024

Adv Funct Materials

View full text Add to dashboard Cite

Bio‐inspired synaptic devices have garnered considerable interest in neuromorphic computing. The Bienenstock‐Cooper‐Munro (BCM) learning rule stands out as one of the most accurate synaptic models, featuring non‐monotonic behavior and threshold sliding effect, crucial for stable learning processes. The direct device strategy for completely mimicking the BCM rule is a tough issue since the current devices lack two competitive working modes within one device. In this work, a dual‐junction synaptic device with opposite built‐in electric fields using a W/WO2/WO3‐x/Au structure is demonstrated. The devices directly mimic two fundamental features of the BCM rule via a delicately‐designed bandgap engineering strategy. Furthermore, the working mechanisms are investigated and the promising potential of dual‐junction synaptic devices is demonstrated for enhancing speech recognition through Convolutional Neural Network (CNN)‐based digital speech recognition with a remarkable accuracy of 98% through a synaptic array. Even for speech recognition with 13% Gaussian noise, the accuracy remained at 83%. These findings provide a promising strategy for developing BCM‐based synaptic devices for neuromorphic computing applications.

show abstract

BCM‐Inspired Synapses Constructed with Barrier‐Modulated Coupling Junctions for Enhancing Speech Recognition

Cai,

Liu,

Wang

et al. 2024

Adv Funct Materials

View full text Add to dashboard Cite

show abstract

Oxide-based bionic hetero-dendritic neuron with capabilities of Bienenstock–Cooper–Munro learning activities

Di,

Huang,

Wang

et al. 2025

J. Mater. Chem. C

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Tuning Bienenstock–Cooper–Munro learning rules in a two-terminal memristor for neuromorphic computing

Abstract: The non-monotonous BCM learning rule with the enhanced depression effect is achieved by applying presynaptic pulses and changing series resistance.

Cited by 2 publications

References 44 publications

BCM‐Inspired Synapses Constructed with Barrier‐Modulated Coupling Junctions for Enhancing Speech Recognition

BCM‐Inspired Synapses Constructed with Barrier‐Modulated Coupling Junctions for Enhancing Speech Recognition

Oxide-based bionic hetero-dendritic neuron with capabilities of Bienenstock–Cooper–Munro learning activities

Contact Info

Product

Resources

About