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

Types of Semiconductors01:20

Types of Semiconductors

997
Intrinsic semiconductors are highly pure materials with no impurities. At absolute zero, these semiconductors behave as perfect insulators because all the valence electrons are bound, and the conduction band is empty, disallowing electrical conduction. The Fermi level is a concept used to describe the probability of occupancy of energy levels by electrons at thermal equilibrium. In intrinsic semiconductors, the Fermi level is positioned at the midpoint of the energy gap at absolute zero. When...
997
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.3K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
3.3K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

810
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
810

You might also read

Related Articles

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

Sort by
Same author

Interface and Thermophysical Properties of <i>R</i>32 Refrigerant.

ACS omega·2026
Same author

Spin-Dominated Electroreduction of Oxygen to Hydrogen Peroxide: A Case Study With Molecular Model Catalysts.

Angewandte Chemie (International ed. in English)·2026
Same author

Superconducting phase diagram of multilayer square-planar nickelates.

Science (New York, N.Y.)·2026
Same author

Dynamic asymmetric strain imprinted into substrates by an oxide thin film.

Science (New York, N.Y.)·2026
Same author

Operando microscopy for neuromorphic hardware.

Nature materials·2026
Same author

Templated growth and intercalation pathways of nickel nanoclusters on graphene/Ir(111).

Nanoscale·2026
Same journal

Erratum for the Research Article "Detecting supramolecular organic nanoparticles during heat wave".

Science (New York, N.Y.)·2026
Same journal

Local signals, systemic decline.

Science (New York, N.Y.)·2026
Same journal

The mechanics of liver regeneration.

Science (New York, N.Y.)·2026
Same journal

Computing in a memory with physics.

Science (New York, N.Y.)·2026
Same journal

Retraction.

Science (New York, N.Y.)·2026
Same journal

Making time.

Science (New York, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Oct 4, 2025

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors
09:59

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors

Published on: June 23, 2018

7.9K

Reconfigurable perovskite nickelate electronics for artificial intelligence.

Hai-Tian Zhang1, Tae Joon Park1, A N M Nafiul Islam2

  • 1School of Materials Engineering, Purdue University, West Lafayette, IN 47907, USA.

Science (New York, N.Y.)
|February 3, 2022
PubMed
Summary
This summary is machine-generated.

Researchers created reconfigurable artificial neurons and synapses using perovskite materials. These brain-inspired computing components can be programmed on demand, advancing adaptive network capabilities.

More Related Videos

Bidirectional Electrical and Optoelectronic Interfaces in Healthy and Ischemic Ex Vivo Rat Hearts
08:33

Bidirectional Electrical and Optoelectronic Interfaces in Healthy and Ischemic Ex Vivo Rat Hearts

Published on: July 18, 2025

316
Monovalent Cation Doping of CH3NH3PbI3 for Efficient Perovskite Solar Cells
08:30

Monovalent Cation Doping of CH3NH3PbI3 for Efficient Perovskite Solar Cells

Published on: March 19, 2017

16.8K

Related Experiment Videos

Last Updated: Oct 4, 2025

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors
09:59

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors

Published on: June 23, 2018

7.9K
Bidirectional Electrical and Optoelectronic Interfaces in Healthy and Ischemic Ex Vivo Rat Hearts
08:33

Bidirectional Electrical and Optoelectronic Interfaces in Healthy and Ischemic Ex Vivo Rat Hearts

Published on: July 18, 2025

316
Monovalent Cation Doping of CH3NH3PbI3 for Efficient Perovskite Solar Cells
08:30

Monovalent Cation Doping of CH3NH3PbI3 for Efficient Perovskite Solar Cells

Published on: March 19, 2017

16.8K

Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Science

Background:

  • Reconfigurable devices enable on-demand programming of electronic circuits.
  • Perovskite nickelates exhibit electronic properties sensitive to hydrogen ion distribution.
  • Artificial neurons and synapses are key components for brain-inspired computing.

Purpose of the Study:

  • To demonstrate the on-demand creation of artificial neurons, synapses, and memory capacitors in perovskite NdNiO3 devices.
  • To investigate the use of these reconfigurable components in reservoir computing and incremental learning.
  • To explore new directions in adaptive networks through on-demand fabrication of computing building blocks.

Main Methods:

  • Utilized post-fabricated perovskite NdNiO3 devices.
  • Employed single-shot electric pulses for device reconfiguration.
  • Integrated experimental data with reservoir computing simulations.
  • Simulated dynamic and static networks for incremental learning tasks.

Main Results:

  • Successfully created reconfigurable artificial neurons, synapses, and memory capacitors.
  • Demonstrated excellent performance in digit recognition and ECG heartbeat classification using memory capacitors in a reservoir computing framework.
  • Showcased superior performance of dynamic networks over static networks in incremental learning scenarios using reconfigurable neurons and synapses.

Conclusions:

  • On-demand reconfiguration of perovskite NdNiO3 devices enables the creation of essential brain-inspired computing components.
  • These reconfigurable components show promise for advanced applications in reservoir computing and adaptive learning.
  • The ability to fabricate artificial neurons and synapses on demand opens new avenues for developing adaptive and intelligent systems.