Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mason's Rule01:20

Mason's Rule

559
Mason's rule is a powerful tool in control systems and signal processing. It simplifies the calculation of transfer functions from signal-flow graphs. This method leverages various elements, including loop gains, forward-path gains, and non-touching loops, to determine the transfer function efficiently.
Loop gain is determined by identifying and tracing a path from a node back to itself. This involves computing the product of branch gains along the loop. Each loop's gain is crucial for...
559
Design Example: Frog Muscle Response01:14

Design Example: Frog Muscle Response

347
A student is tasked to work on an intriguing experiment involving an RL (Resistor-Inductor) circuit to study the muscle response of a frog's leg to electrical stimulation. The RL circuit plays a crucial role in this experiment, providing the means to control and measure the electrical impulses that trigger muscle contraction.
When the switch connecting the RL circuit is closed, a brief muscle contraction is observed. This is because, at a steady state, the inductor acts like a short...
347
MOS Capacitor01:25

MOS Capacitor

1.1K
A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
The metal gate is typically made from highly conductive materials such as aluminum or polysilicon. Beneath the metal gate lies a thin layer of...
1.1K
Node Analysis for AC Circuits01:14

Node Analysis for AC Circuits

398
Consider an angioplasty system featuring a catheter equipped with a turbine, a critical tool for removing plaque deposits from coronary arteries. This intricate medical device operates using a circuit model reminiscent of a dual-node RLC circuit powered by a current-controlled voltage source.
To unravel the complexities of this system, nodal analysis is employed, a powerful technique founded on Kirchhoff's current law (KCL), which remains valid for phasors. AC circuits can effectively be...
398
Neural Circuits01:25

Neural Circuits

1.8K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.8K
Biasing of P-N Junction01:16

Biasing of P-N Junction

1.0K
The operation of a p-n junction diode involves various biasing conditions, including forward bias, reverse bias, and equilibrium.
In equilibrium, no external voltage is applied across the p-n junction. The depletion region is formed at the junction interface due to the diffusion of carriers, which leaves behind charged dopants, acceptors on the p-side, and donors on the n-side. These immobile charges create an electric field that prevents further diffusion of carriers. The related energy band...
1.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Distribution, migration, and source of anthropogenic <sup>236</sup>U in different forest vegetation zones of Changbai Mountain.

Journal of hazardous materials·2026
Same author

Loss of the type VII secretion ATPase EssC promotes biofilm formation of <i>Staphylococcus</i> <i>aureus</i> under acidic stress.

Biofilm·2026
Same author

Distribution, enrichment and provenance of <sup>137</sup>Cs, <sup>237</sup>Np and <sup>239+240</sup>Pu in shell-sand sediments from the Binzhou Shell Dike Island, China.

Marine pollution bulletin·2026
Same author

Oncogenic KRAS Represses the Dependence Receptor UNC5C via ERK2-FOS Signaling in Non-Small Cell Lung Cancer.

The Journal of biological chemistry·2026
Same author

Fe<sup>2+</sup>/O<sub>2</sub>-based advanced oxidation process coupled with air sparging/ circulation well for in-situ groundwater remediation: Synergistic technologies construction and feasibility assessment.

Journal of hazardous materials·2026
Same author

Tuning Redox Potentials in NASICON Cathode via Covalent Lattice Modulation.

Angewandte Chemie (International ed. in English)·2026

Related Experiment Video

Updated: Oct 8, 2025

A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

9.1K

Mapping the BCPNN Learning Rule to a Memristor Model.

Deyu Wang1, Jiawei Xu1, Dimitrios Stathis2

  • 1State Key Laboratory of ASIC and System, School of Information Science and Technology, Fudan University, Shanghai, China.

Frontiers in Neuroscience
|December 27, 2021
PubMed
Summary

This study demonstrates the first memristor-based implementation of the Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. This novel approach overcomes the von Neumann bottleneck for efficient artificial synapse development.

Keywords:
Bayesian Confidence Propagation Neural Network (BCPNN)analog neuromorphic hardwarelearning rulememristornonlinear dopant drift phenomenonspiking neural networkssynaptic state update

More Related Videos

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

8.0K
Modeling Biological Membranes with Circuit Boards and Measuring Electrical Signals in Axons: Student Laboratory Exercises
13:56

Modeling Biological Membranes with Circuit Boards and Measuring Electrical Signals in Axons: Student Laboratory Exercises

Published on: January 18, 2011

22.9K

Related Experiment Videos

Last Updated: Oct 8, 2025

A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

9.1K
Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

8.0K
Modeling Biological Membranes with Circuit Boards and Measuring Electrical Signals in Axons: Student Laboratory Exercises
13:56

Modeling Biological Membranes with Circuit Boards and Measuring Electrical Signals in Axons: Student Laboratory Exercises

Published on: January 18, 2011

22.9K

Area of Science:

  • Neuroscience and Computer Engineering
  • Artificial Intelligence and Neuromorphic Computing

Background:

  • The Bayesian Confidence Propagation Neural Network (BCPNN) is a biologically plausible neural network model with applications in cortical associative memory and machine learning.
  • Conventional digital implementations of BCPNN face challenges due to the von Neumann bottleneck, limiting synaptic storage and access efficiency.
  • Memristors offer a promising solution for artificial synapses by integrating computation and storage, potentially overcoming the von Neumann bottleneck.

Purpose of the Study:

  • To map and implement the BCPNN learning rule onto a memristor-based architecture.
  • To demonstrate the feasibility of using memristors for efficient BCPNN computation and storage.
  • To overcome the limitations of traditional digital BCPNN implementations.

Main Methods:

  • Developed a memristor model to simulate the BCPNN learning rule, exploiting the nonlinear dopant drift phenomenon for synaptic state variable decay.
  • Designed and simulated a mixed-signal, analog-domain architecture for BCPNN implementation using memristors.
  • Verified the BCPNN learning rule's consistency with the memristor-based solution using Matlab simulations.

Main Results:

  • Achieved a high correlation coefficient (0.99) between the memristor-based solution and the BCPNN learning rule in Matlab simulations.
  • Demonstrated a good emulation effect of the BCPNN learning rule in a SPICE simulation environment with a correlation coefficient of 0.98.
  • Validated the feasibility of mapping the BCPNN learning rule to in-circuit computation using memristors.

Conclusions:

  • The memristor-based implementation successfully emulates the BCPNN learning rule, overcoming the von Neumann bottleneck.
  • This work validates the feasibility of using memristors for efficient BCPNN, paving the way for real-time brain emulation engines.
  • The proposed architecture offers a significant step towards more efficient and powerful neuromorphic computing systems.