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

MOS Capacitor01:25

MOS Capacitor

782
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...
782
Neuroplasticity01:01

Neuroplasticity

361
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
361
The Resting Membrane Potential01:21

The Resting Membrane Potential

132.3K
Overview
132.3K
Long-term Potentiation01:25

Long-term Potentiation

2.8K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when...
2.8K
Design Example: Frog Muscle Response01:14

Design Example: Frog Muscle Response

236
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...
236

You might also read

Related Articles

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

Sort by
Same author

A Generalized Responsible AI Framework for Trustworthy Clinical Prediction: Explainability, Fairness, Performance, and Uncertainty in Alzheimer's Disease Modeling.

Healthcare (Basel, Switzerland)·2026
Same author

Efficacy and safety of IgG-pathway inhibitors in adult immune thrombocytopenia: A systematic review and meta-analysis with subgroup analyses of FcRn and SYK inhibitors.

Cytokine·2026
Same author

Inducing nonlinear conductance and emergent memristance in open pores using blockers.

Faraday discussions·2026
Same author

Electromechanically induced membrane restructuring enables learning and memory.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Multi-Material Droplet-Based Hydrogel Threads for Extrusion 3D Printing.

Small methods·2025
Same author

A hybrid deep learning and rule-based model for smart weather forecasting and crop recommendation using satellite imagery.

Scientific reports·2025

Related Experiment Video

Updated: Jul 5, 2025

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

7.8K

Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity.

Nicholas X Armendarez1, Ahmed S Mohamed1, Anurag Dhungel2

  • 1Department of Mechanical Engineering, The Pennsylvania State University, 336 Reber Building, University Park, Pennsylvania 16802, United States.

ACS Applied Materials & Interfaces
|January 25, 2024
PubMed
Summary
This summary is machine-generated.

Researchers developed novel memristors for reservoir computing (RC). These distinct memristors enable more accurate data processing with less energy, paving the way for advanced physical computing systems.

Keywords:
artificial synapsesion channelslipid bilayersmemristorsreservoir computingvolatile memory

More Related Videos

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

9.0K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

Related Experiment Videos

Last Updated: Jul 5, 2025

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

7.8K
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

9.0K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

Area of Science:

  • Materials Science
  • Computer Engineering
  • Computational Neuroscience

Background:

  • Reservoir computing (RC) requires physical analogue devices for efficient information processing.
  • Dynamic memristors show promise for RC due to their nonlinear and memory dynamics.
  • Existing RC implementations using identical memristors lack a rich state space, necessitating complex workarounds.

Purpose of the Study:

  • To demonstrate controllable and diverse dynamics in ion-channel-based memristors for improved RC performance.
  • To investigate the efficacy of reservoir layers composed of distinct memristors for enhanced signal processing.
  • To establish a foundation for next-generation physical RC systems utilizing diverse memristor dynamics.

Main Methods:

  • Utilized ion-channel-based memristors with voltage-dependent dynamics.
  • Controllably tuned memristor dynamics via voltage or ion channel concentration adjustment.
  • Constructed and tested reservoir layers with small numbers of distinct memristors in simulations and experiments.

Main Results:

  • Achieved significantly higher predictive and classification accuracies with a single data encoding using varied memristor reservoirs.
  • Demonstrated a normalized mean square error of 1.5 × 10-3 in a nonlinear dynamical system prediction task with five distinct memristors.
  • Attained 96.5% accuracy in a neural activity classification task using a reservoir of only three distinct memristors.

Conclusions:

  • Reservoir layers built with a small number of distinct memristors offer superior performance in temporal tasks.
  • Controllable memristor dynamics enable the creation of diverse reservoir properties for enhanced signal processing.
  • This research advances physical RC systems by leveraging the complex dynamics of varied memristor components.