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

Neuroplasticity01:01

Neuroplasticity

322
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.
322

You might also read

Related Articles

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

Sort by
Same author

Diazo-6Bx, a Six-Branched Diazo Cross-Linker, Enables High-Fidelity Patterning for Solution-Processed Electronics with Stable Operation.

ACS nano·2026
Same author

Disorder-mediated non-equilibrium photocurrent redistribution enables homeostatic synaptic conditioning in AgBiS<sub>2</sub> heterostructure.

Nature communications·2026
Same author

Ag<sub>2</sub>Se/conjugated polyelectrolyte heterojunction films for high-performance flexible thermoelectrics.

Materials horizons·2026
Same author

Quantum dots with photopolymerisable ligands for green-solvent direct photolithography.

Materials horizons·2026
Same author

Imparting Biodegradability to Highly-Efficient Upconversion Nanoparticles via Facet-Selective Zirconium Doping.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Flexible Dielectric Acoustic Resonator Patch for Tissue Regeneration.

Advanced materials (Deerfield Beach, Fla.)·2026

Related Experiment Video

Updated: Jun 23, 2025

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

Toward human-like adaptability in robotics through a retention-engineered synaptic control system.

Chan Kim1, Dong Gue Roe2, Dong Un Lim3

  • 1Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea.

Science Advances
|June 26, 2024
PubMed
Summary

Researchers developed novel synaptic devices for robots, enabling human-like learning and adaptation without complex computation. This biomimetic approach enhances robotic adaptability through engineered retention properties and parallel processing.

More Related Videos

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.3K
Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.1K

Related Experiment Videos

Last Updated: Jun 23, 2025

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
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.3K
Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.1K

Area of Science:

  • Biomimetic Robotics
  • Neuro-inspired Computing
  • Materials Science

Background:

  • Advanced robots excel at mimicking human form and motion but lack adaptive learning capabilities.
  • Current robotic systems often require complex computational architectures for learning.
  • A need exists for more biologically plausible and computationally efficient learning systems in robotics.

Purpose of the Study:

  • To propose an innovative control system for robots that simulates human-like learning and adaptation.
  • To develop a system that reduces computational complexity by using parallel-processable synaptic devices.
  • To emulate a human-like workout process demonstrating adaptive feedback mechanisms.

Main Methods:

  • Utilized retention-engineered synaptic devices with adjustable Ag/AgCl ink content to modulate electrical properties.
  • Engineered ion gel electrolytes to facilitate unrestricted ion movement, enabling device-level parallel processing.
  • Integrated these synaptic devices with actuators to create a biomimetic control system.

Main Results:

  • Demonstrated modulation of synaptic device retention properties by controlling Ag/AgCl ink deposition.
  • Achieved enhanced signal multiplexing and parallel processing through ion gel characteristics.
  • Successfully emulated a human-like workout process, showcasing adaptive responses to stimuli.

Conclusions:

  • The proposed synaptic device-based control system offers a pathway to human-like learning in robots.
  • This approach significantly reduces system complexity compared to traditional computational methods.
  • The findings highlight the potential of biomimicry in developing more adaptable and intelligent robotic systems.