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

Action Potential: Phases of Stimulation01:28

Action Potential: Phases of Stimulation

17.2K
The action potential is a complex electrical event that occurs in excitable cells, such as neurons and muscle cells. It consists of several distinct phases, each with specific characteristics.
Resting Phase:
In this phase, the cell's membrane is at its resting potential, typically around -70 millivolts (mV) for neurons. Inside the cell, there is a higher concentration of potassium ions (K+) and a lower concentration of sodium ions (Na+). Voltage-gated sodium channels are closed, and...
17.2K
Neural Circuits01:25

Neural Circuits

3.2K
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...
3.2K
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

4.2K
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....
4.2K
Electrochemical Gradient and Channel Proteins: An Overview01:21

Electrochemical Gradient and Channel Proteins: An Overview

5.2K
An electrochemical gradient is a fundamental concept in biology and chemistry. It regulates the movement of ions across cell membranes. This movement is influenced by two factors:
The electrical gradient: The electrical gradient across cell membranes refers to the difference in electric charge between the inside and outside of a cell.  This difference drives the movement of ions towards or away from the cells. For instance, if the inside of the cell is more negatively charged relative to...
5.2K
Phase Transitions02:31

Phase Transitions

23.6K
Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to...
23.6K
Phase Transitions01:21

Phase Transitions

43
A phase transition is the process in which a substance changes from one state of matter to another, like from a solid to a liquid, liquid to gas, or vice versa, at a specific temperature and under given pressure conditions. This change is spontaneous and is affected by alterations in temperature and pressure. These parameters impact the strength of the forces between molecules (intermolecular forces) in the substance.During a phase transition, both the initial and final phases of the substance...
43

You might also read

Related Articles

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

Sort by
Same author

Supernetwork-based efficient mapping of deep learning applications to mixed-precision hardware using model adaptation.

Nature communications·2026
Same author

From Flow Modulation to Resection: First in Human Combination of Liver Venous Deprivation and Balloon-Occluded Chemoembolization (LVD-B-TACE): A Case Report Demonstrating a Novel Sequential Concept.

Journal of hepatocellular carcinoma·2026
Same author

Artificial intelligence-assisted chest radiograph interpretation in Role 2 military field hospital settings: a controlled experimental study.

Trauma surgery & acute care open·2025
Same author

Analogue speech recognition based on physical computing.

Nature·2025
Same author

Training of physical neural networks.

Nature·2025
Same author

Phase-Change Memory for In-Memory Computing.

Chemical reviews·2025
Same journal

Bridging nanotechnology and mechanobiology.

Nature nanotechnology·2026
Same journal

Coherent 2D/3D van der Waals epitaxy enables single-crystal perovskite heterostructures.

Nature nanotechnology·2026
Same journal

Coherent 2D-3D van der Waals perovskite epitaxial heterostructures.

Nature nanotechnology·2026
Same journal

Ultrafast, reconfigurable all-optical beam steering and spatial light modulation.

Nature nanotechnology·2026
Same journal

A high-energy hydrogen radical initiates efficient electrosynthesis of urea from CO<sub>2</sub> and N<sub>2</sub>.

Nature nanotechnology·2026
Same journal

Machine-intelligent multimodal algebot for intracavitary chemotherapy.

Nature nanotechnology·2026
See all related articles

Related Experiment Video

Updated: Mar 21, 2026

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
06:22

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

Published on: August 28, 2019

5.6K

Stochastic phase-change neurons.

Tomas Tuma1, Angeliki Pantazi1, Manuel Le Gallo1,2

  • 1IBM Research-Zurich, CH-8803 Rüschlikon, Switzerland.

Nature Nanotechnology
|May 17, 2016
PubMed
Summary
This summary is machine-generated.

Researchers developed artificial neurons using phase-change materials. These novel devices mimic brain functions, integrating signals on a nanosecond timescale for efficient neuromorphic computing.

More Related Videos

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

12.5K
C. elegans Tracking and Behavioral Measurement
07:36

C. elegans Tracking and Behavioral Measurement

Published on: November 17, 2012

20.0K

Related Experiment Videos

Last Updated: Mar 21, 2026

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
06:22

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

Published on: August 28, 2019

5.6K
Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

12.5K
C. elegans Tracking and Behavioral Measurement
07:36

C. elegans Tracking and Behavioral Measurement

Published on: November 17, 2012

20.0K

Area of Science:

  • Neuroscience and Materials Science
  • Development of artificial neurons
  • Neuromorphic computing systems

Background:

  • Artificial neuromorphic systems are crucial for brain research and creating brain-like computers.
  • Memristive devices, like electroionics and phase-change types, are explored as nanoscale synapses for efficiency.
  • Scalable artificial neuron realization remains a challenge.

Purpose of the Study:

  • To demonstrate a novel artificial neuron using chalcogenide-based phase-change materials.
  • To represent membrane potential using the phase configuration of a nanoscale phase-change device.
  • To achieve temporal integration of postsynaptic potentials on a nanosecond timescale.

Main Methods:

  • Exploiting the physics of reversible amorphous-to-crystal phase transitions in phase-change materials.
  • Utilizing melt-quench-induced atomic reconfiguration for neuron reset and stochasticity.
  • Demonstrating artificial neuron and population functionality.

Main Results:

  • Artificial neurons were created using phase-change materials, representing membrane potential via phase configuration.
  • Temporal integration of postsynaptic potentials was achieved on a nanosecond timescale.
  • The inherent stochasticity of the reset process was observed due to atomic reconfiguration.
  • The artificial neurons were successfully applied to detect temporal correlations and for sub-Nyquist signal representation.

Conclusions:

  • Chalcogenide-based phase-change materials offer a viable route to scalable artificial neuron realization.
  • These artificial neurons enable efficient temporal signal processing and stochastic computation.
  • The developed phase-change neurons show promise for advanced neuromorphic applications.