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

The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

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.
Ligand-Gated Ion Channel Receptor: Gating Mechanism01:30

Ligand-Gated Ion Channel Receptor: Gating Mechanism

Ligand-gated ion channels are transmembrane proteins that play a vital role in intercellular communication and functions of the nervous system. They allow the influx of ions across the membrane once the neurotransmitter binds, allowing the subsequent transmission of electrical excitation across the neurons. Other ligand-gated ion channels, like the γ-aminobutyric acid (GABA) receptor, permit anions like chloride into the cells on the binding of the GABA molecule. Their entry into the cell...
Electrochemical Gradient and Channel Proteins: An Overview01:21

Electrochemical Gradient and Channel Proteins: An Overview

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 the...
Mechanically-gated Ion Channels01:12

Mechanically-gated Ion Channels

Mechanically-gated ion channels are proteins found in eukaryotic and prokaryotic cell membranes that open in response to mechanical stress. Tension, compression, swelling, and shear stress can alter the conformation of the protein, opening a transmembrane channel that allows the passage of ions for signal transmission. In eukaryotes, mechanically-gated channels are distributed in several regions like the neurons, lungs, skin, bladder, and heart, where they play critical roles in numerous...
Mechanically-gated Ion Channels01:12

Mechanically-gated Ion Channels

Mechanically-gated ion channels are proteins found in eukaryotic and prokaryotic cell membranes that open in response to mechanical stress. Tension, compression, swelling, and shear stress can alter the conformation of the protein, opening a transmembrane channel that allows the passage of ions for signal transmission. In eukaryotes, mechanically-gated channels are distributed in several regions like the neurons, lungs, skin, bladder, and heart, where they play critical roles in numerous...
Integration of Synaptic Events01:28

Integration of Synaptic Events

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...

You might also read

Related Articles

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

Sort by
Same author

Neuromorphic computing at scale.

Nature·2025
Same author

Design and Analysis of a Neuromemristive Reservoir Computing Architecture for Biosignal Processing.

Frontiers in neuroscience·2016
Same author

The inhibitory and apoptotic effects of docetaxel-loaded mesoporous magnetic colloidal nanocrystal clusters on bladder cancer T24 cells in vitro.

Journal of biomedical nanotechnology·2014
Same author

Novel experimental model of enlarging abdominal aortic aneurysm in rabbits.

Journal of vascular surgery·2014
Same author

Optical coherence tomographic evaluation of transplant coronary artery vasculopathy with correlation to cellular rejection.

Circulation. Cardiovascular interventions·2014
Same author

Impact of multiple complex plaques on short- and long-term clinical outcomes in patients presenting with ST-segment elevation myocardial infarction (from the Harmonizing Outcomes With Revascularization and Stents in Acute Myocardial Infarction [HORIZONS-AMI] Trial).

The American journal of cardiology·2014

Related Experiment Video

Updated: Jul 3, 2026

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

Linking device dynamics to neural network performance in ionically gated synaptic transistors.

Nithil Harris Manimaran1, Huayuan Han2, Cory Merkel3

  • 1Microsystems Engineering, Rochester Institute of Technology, Rochester, NY, 14623, USA.

Scientific Reports
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

Neuromorphic computing uses artificial synapses in ionically gated transistors. Optimizing device characteristics like conductance states and nonlinearity is key for accurate artificial neural network (ANN) performance in image classification.

Keywords:
Artificial Neural NetworkConductance StatesElectric Double LayerIonically Gated TransistorsNeuromorphic ComputingWeight Update Nonlinearity

More Related Videos

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics
11:33

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics

Published on: January 19, 2018

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

Related Experiment Videos

Last Updated: Jul 3, 2026

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

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics
11:33

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics

Published on: January 19, 2018

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

Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Science

Background:

  • Neuromorphic computing aims to mimic the brain's efficiency using artificial synapses.
  • Ionically gated transistors offer promising synaptic behavior due to natural ion dynamics.
  • Understanding device characteristics' impact on neural network performance is crucial.

Purpose of the Study:

  • Investigate how device characteristics (conductance states, nonlinearity) affect artificial neural network (ANN) performance.
  • Link device physics to ANN simulations for MoS2-based synaptic transistors.
  • Develop methods to linearize synaptic weight updates for improved accuracy.

Main Methods:

  • Utilized a combined experimental and modeling framework for MoS2-based ionically gated synaptic transistors.
  • Performed hardware-aware ANN simulations for image classification tasks (MNIST, FMNIST, KMNIST).
  • Developed a physics-informed transient model and pulse-engineering algorithm to linearize weight updates.

Main Results:

  • Increasing conductance states led to a trade-off between weight resolution and update nonlinearity.
  • Optimal weight resolution, not just more states, determined classification accuracy.
  • Linearized synaptic weight updates improved ANN accuracy across various datasets and state numbers.

Conclusions:

  • Device characteristics significantly impact ANN performance in neuromorphic applications.
  • A physics-informed approach can linearize synaptic updates, enhancing device utility.
  • Established a quantitative link between ionically gated transistor physics and neural network performance.