Jove
Visualize
Contact Us

Related Concept Videos

MOS Capacitor01:25

MOS Capacitor

1.9K
A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
The metal gate is typically made from highly conductive materials such as aluminum or polysilicon. Beneath the metal gate lies a thin layer of...
1.9K
Resting Membrane Potential01:24

Resting Membrane Potential

26.1K
The relative difference in electrical charge, or voltage, between the inside and the outside of a cell membrane, is called the membrane potential. It is generated by differences in permeability of the membrane to various ions and the concentrations of these ions across the membrane.
The Inside of a Neuron is More Negative
The membrane potential of a cell can be measured by inserting a microelectrode into a cell and comparing the charge to a reference electrode in the extracellular fluid. The...
26.1K
Resting Membrane Potential01:24

Resting Membrane Potential

9.1K
9.1K
Potentiometry: Membrane Electrodes01:15

Potentiometry: Membrane Electrodes

2.4K
Membrane electrodes, also known as p-ion electrodes, use membranes that selectively interact with free analyte ions, generating a potential difference across the membrane. The resulting membrane potential, known as the asymmetry potential, is not zero even when analyte concentrations on both sides of the membrane are equal. The membrane's response is typically not selective to a single analyte but proportional to the concentration of all ions in the sample solution capable of interacting at...
2.4K
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

4.3K
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....
4.3K
MOSFET01:16

MOSFET

1.8K
The Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) plays a pivotal role in modern electronics thanks to its versatility and efficiency in controlling electrical currents. This device, also known as IGFET, MISFET, and MOSFET, has three main terminals: the Source, Drain, and Gate. MOSFETs are classified into n-channel or p-channel types based on the doping characteristics of their substrate and the source or drain regions.
In an n-MOSFET, the structure includes n-type source and drain...
1.8K

You might also read

Related Articles

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

Sort by
Same author

Extremely multistable matryoshka systems with hidden attractors.

Chaos (Woodbury, N.Y.)·2025
Same author

The Structural Similarity Can Identify the Presence of Noise in Video Data from Unmanned Vehicles.

Journal of imaging·2025
Same author

Microbial Biofilms: Features of Formation and Potential for Use in Bioelectrochemical Devices.

Biosensors·2024
Same author

Targeted Formation of Biofilms on the Surface of Graphite Electrodes as an Effective Approach to the Development of Biosensors for Early Warning Systems.

Biosensors·2024
Same author

Magnetic Flux Sensor Based on Spiking Neurons with Josephson Junctions.

Sensors (Basel, Switzerland)·2024
Same author

Synthesis under Normal Conditions and Morphology and Composition of AlF<sub>3</sub> Nanowires.

Nanomaterials (Basel, Switzerland)·2023
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 Experiment Video

Updated: Apr 15, 2026

A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

9.5K

Spiking Neuron with Sensing Coil Based on a Volatile Memristor.

Timur Karimov1, Vyacheslav Rybin2, Vasiliy Pchelko2

  • 1Youth Research Institute, Saint Petersburg Electrotechnical University "LETI", Professora Popova St. 5F, Saint Petersburg 197022, Russia.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel memristor-resistor-inductor-capacitor (MRLC) neuron for energy-efficient edge intelligence. This spiking neuron integrates electromagnetic sensing, enabling direct proximity detection and advanced neural dynamics.

Keywords:
electromagnetic sensingmetal detectionneuromorphic hardwareproximity sensingsensory neuronspiking neural networksvolatile memristor

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

8.4K
Imaging Membrane Potential with Two Types of Genetically Encoded Fluorescent Voltage Sensors
09:57

Imaging Membrane Potential with Two Types of Genetically Encoded Fluorescent Voltage Sensors

Published on: February 4, 2016

11.4K

Related Experiment Videos

Last Updated: Apr 15, 2026

A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

9.5K
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

8.4K
Imaging Membrane Potential with Two Types of Genetically Encoded Fluorescent Voltage Sensors
09:57

Imaging Membrane Potential with Two Types of Genetically Encoded Fluorescent Voltage Sensors

Published on: February 4, 2016

11.4K

Area of Science:

  • Neuromorphic Engineering
  • Spiking Neural Networks
  • Edge Intelligence Hardware

Background:

  • The integration of sensing and processing is crucial for energy-efficient edge intelligence.
  • Existing spiking neuron models like the leaky integrate-and-fire (LIF) neuron lack inherent sensory capabilities.
  • Developing hardware that directly translates sensory input into neural signals is a key research challenge.

Purpose of the Study:

  • To present a novel hardware implementation of a sensory neuron.
  • To embed electromagnetic sensing directly into neuronal dynamics.
  • To demonstrate a metal-sensitive proximity sensor with spiking output.

Main Methods:

  • A novel sensory neuron was designed by coupling a volatile memristor with an LC tank circuit, creating a memristor-resistor-inductor-capacitor (MRLC) neuron.
  • The MRLC neuron's design embeds electromagnetic sensing directly into its dynamics.
  • The functionality was validated through both circuit simulations and physical experiments.

Main Results:

  • The MRLC neuron successfully functions as a metal-sensitive proximity sensor, generating spiking outputs.
  • The circuit exhibits diverse dynamical behaviors, including regular spiking, bursting (2-5 spikes/burst), and quasi-chaotic activity.
  • The neuron demonstrates sensing memory through hysteresis-like multistability, surpassing basic LIF neuron capabilities.

Conclusions:

  • The proposed MRLC neuron represents a significant advancement in energy-efficient spiking edge intelligence by integrating sensing and processing.
  • This hardware implementation enables direct transduction of proximity information into spike trains, offering richer dynamics than traditional models.
  • The MRLC neuron's capabilities pave the way for more sophisticated and energy-efficient neuromorphic systems at the edge.