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

Integration of Synaptic Events01:28

Integration of Synaptic Events

1.4K
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...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Effects of the total thickness of multilayer thermite materials Al/CuO on the combustion mode and morphology of reaction products.

Nanotechnology·2026
Same author

Atomic-Scale Engineering of Ge-Sb-Te Compounds: Ge Vacancies in Bulk GeSb<sub>4</sub>Te<sub>7</sub> and Layer Sliding in GeSb<sub>2</sub>Te<sub>4</sub> Monolayers.

Nanomaterials (Basel, Switzerland)·2026
Same author

Self-Organized Memristive Ensembles of Nanoparticles Below the Percolation Threshold: Switching Dynamics and Phase Field Description.

Nanomaterials (Basel, Switzerland)·2023
Same author

Janus Type Monolayers of S-MoSiN<sub>2</sub> Family and Van Der Waals Heterostructures with Graphene: DFT-Based Study.

Nanomaterials (Basel, Switzerland)·2022
Same author

Temperature-Dependent Fractional Dynamics in Pseudo-Capacitors with Carbon Nanotube Array/Polyaniline Electrodes.

Nanomaterials (Basel, Switzerland)·2022
Same author

First-principles study of graphenylene/MoX<sub>2</sub> (X = S, Te, and Se) van der Waals heterostructures.

Physical chemistry chemical physics : PCCP·2021

Related Experiment Video

Updated: May 23, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.0K

Intermittent spike train processing through fractional leaky integrate-and-fire neuromorphic unit.

R T Sibatov1,2, A K Gavrilova3, A I Savitskiy1,2

  • 1Scientific-Manufacturing Complex "Technological Centre," Moscow, Russia.

Chaos (Woodbury, N.Y.)
|May 22, 2025
PubMed
Summary

We introduce a fractional-order leaky integrate-and-fire (LIF) model with long-term memory for processing scale-invariant signals. A novel carbon nanotube transistor hardware implementation demonstrates its potential for neuromorphic computing.

More Related Videos

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

11.4K
A Procedure for Implanting Organized Arrays of Microwires for Single-unit Recordings in Awake, Behaving Animals
10:58

A Procedure for Implanting Organized Arrays of Microwires for Single-unit Recordings in Awake, Behaving Animals

Published on: February 14, 2014

13.2K

Related Experiment Videos

Last Updated: May 23, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.0K
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

11.4K
A Procedure for Implanting Organized Arrays of Microwires for Single-unit Recordings in Awake, Behaving Animals
10:58

A Procedure for Implanting Organized Arrays of Microwires for Single-unit Recordings in Awake, Behaving Animals

Published on: February 14, 2014

13.2K

Area of Science:

  • Neuroscience
  • Complex Systems
  • Materials Science

Background:

  • The leaky integrate-and-fire (LIF) model is a cornerstone for simulating neuronal behavior in spiking networks.
  • Generalized LIF models offer enhanced features like adaptation and noise, but capturing long-range temporal correlations remains a challenge.
  • Power-law dynamics are crucial for processing intermittent, scale-invariant signals, mimicking long-term memory effects.

Purpose of the Study:

  • To investigate the fractional-order extension of the LIF model for emulating long-term memory.
  • To analyze the statistical response of this model to fractional Poisson process inputs.
  • To develop a hardware implementation of the fractional-order LIF model for neuromorphic applications.

Main Methods:

  • Developed a fractional-order leaky integrate-and-fire (LIF) model incorporating fractional derivatives.
  • Evaluated the statistical properties of the model's response to flickering input voltage pulses characterized by a fractional Poisson process.
  • Fabricated a microscale transistor using single-walled carbon nanotubes and an electrolyte gate to realize the fractional-order dynamics.

Main Results:

  • The fractional-order LIF model exhibits power-law dynamics, effectively emulating long-term memory.
  • Statistical analysis characterized the model's response to fractional Poisson process inputs.
  • The fabricated carbon nanotube transistor successfully demonstrated fractional-order dynamics.

Conclusions:

  • The fractional-order LIF model is a promising framework for simulating neuronal dynamics with long-term memory.
  • The developed hardware provides a viable pathway for creating neuromorphic spiking networks capable of processing scale-invariant signals.
  • This approach facilitates the development of efficient hardware for artificial intelligence and signal processing applications.