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

Long-term Potentiation01:25

Long-term Potentiation

2.9K
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...
2.9K
Neuroplasticity01:01

Neuroplasticity

884
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.
884
Plasticity00:58

Plasticity

2.5K
Plasticity is the property where an object loses its elasticity and undergoes irreversible deformation, even after the deformation forces are eliminated. If a material deforms irreversibly without increasing stress or load, then this is called ideal plasticity. For example, when a force is applied to an aluminum rod, it changes its shape, but it does not return to its original shape once the force is removed. Plastic deformation or ductility is thus a permanent deformation or change in the...
2.5K
Integration of Synaptic Events01:28

Integration of Synaptic Events

2.3K
Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
2.3K
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
Neural Circuits01:25

Neural Circuits

1.7K
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...
1.7K

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

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 author

The growing memristor industry.

Nature·2025
Same author

The inherent adversarial robustness of analog in-memory computing.

Nature communications·2025

Related Experiment Video

Updated: Sep 28, 2025

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity
11:56

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

15.7K

Phase-change memtransistive synapses for mixed-plasticity neural computations.

Syed Ghazi Sarwat1, Benedikt Kersting2, Timoleon Moraitis2

  • 1IBM Research - Europe, Rüschlikon, Switzerland. ghs@zurich.ibm.com.

Nature Nanotechnology
|March 29, 2022
PubMed
Summary

Researchers developed a novel memtransistive synapse capable of mimicking short-term and long-term plasticity for neuromorphic computing. This artificial synapse enables tunable plasticity essential for advanced learning and memory applications.

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
Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.6K

Related Experiment Videos

Last Updated: Sep 28, 2025

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity
11:56

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

15.7K
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
Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.6K

Area of Science:

  • Neuromorphic Engineering
  • Materials Science
  • Computational Neuroscience

Background:

  • Synaptic plasticity is crucial for learning and memory in mammalian nervous systems.
  • Neuromorphic computing requires artificial synapses that emulate multi-timescale plasticity.
  • Existing hardware often lacks tunable long-term and short-term plasticity.

Purpose of the Study:

  • To introduce a novel phase-change memtransistive synapse.
  • To implement tunable multi-timescale plasticity in artificial synapses.
  • To demonstrate the synapse's application in neural network accelerators.

Main Methods:

  • Utilized a phase-change memtransistive synapse architecture.
  • Leveraged non-volatility of phase configurations and volatility of field-effect modulation.
  • Implemented short-term spike-timing-dependent plasticity (STDP) and Hopfield neural networks.

Main Results:

  • Demonstrated tunable plasticity using mixed non-volatile and volatile mechanisms.
  • Successfully emulated short-term STDP for dynamic environment modeling.
  • Showcased memtransistive synapse efficacy in Hopfield network accelerators for optimization problems.

Conclusions:

  • The developed memtransistive synapse effectively mimics multi-timescale plasticity.
  • This technology advances neuromorphic computing capabilities for learning and complex problem-solving.
  • Offers a promising hardware solution for next-generation artificial intelligence.