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

3.5K
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....
3.5K

You might also read

Related Articles

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

Sort by
Same author

Ultra-flexible wireless endovascular stimulator for cortical simulation.

Journal of neural engineering·2026
Same author

The Influence of Recording Duration and Vigilance State on High-Frequency Oscillation Characterization in Epilepsy.

Neurology·2026
Same author

Perceived direction of glass patterns can flip by 90°: A neural model.

Vision research·2026
Same author

A neural mass modelling framework for evaluating EEG source localisation of seizure activity.

Journal of neural engineering·2026
Same author

Investigation of stress hormones across multiday seizure cycles.

Brain communications·2026
Same author

Toward a fully wireless endovascular neural interface: Evaluating power transfer efficacy.

PloS one·2026

Related Experiment Video

Updated: Nov 30, 2025

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.1K

Adaptive Surround Modulation of MT Neurons: A Computational Model.

Parvin Zarei Eskikand1, Tatiana Kameneva1,2, Anthony N Burkitt1

  • 1Department of Biomedical Engineering, The University of Melbourne, Parkville, VIC, Australia.

Frontiers in Neural Circuits
|November 16, 2020
PubMed
Summary

Computational models reveal that the surround receptive fields of Middle Temporal (MT) neurons dynamically adjust their function. This modulation depends on stimulus properties, not fixed neuron characteristics, impacting motion perception.

Keywords:
adaptive surround modulationmiddle temporal (MT)motion perceptionneural modelvision

More Related Videos

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.2K
3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.2K

Related Experiment Videos

Last Updated: Nov 30, 2025

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.1K
Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.2K
3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.2K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Visual Processing

Background:

  • Spiking visual neurons possess classical receptive fields (CRF) and extra-classical receptive fields (ECRF).
  • The ECRF modulates neuronal responses to stimuli within the CRF without directly eliciting spikes.
  • Middle Temporal (MT) area neurons, specialized for motion, exhibit directionally antagonistic or facilitatory surrounds.

Purpose of the Study:

  • To develop a computational model of MT neurons replicating surround modulation.
  • To elucidate the underlying mechanisms of ECRF modulatory effects.
  • To investigate whether surround effects are neuron- or stimulus-dependent.

Main Methods:

  • Developed a computational model of primate MT neurons.
  • Incorporated computational building blocks correlating with visual pathway cell types.
  • Simulated responses to various visual stimuli to analyze surround interactions.

Main Results:

  • The model successfully replicated the switch between antagonistic and facilitatory surround effects based on stimulus characteristics.
  • Model demonstrated that surround categorization is stimulus-dependent, not an intrinsic neuron property.
  • ECRFs exhibited contrast-dependent alterations in center-surround interactions, aligning with neurophysiological data.

Conclusions:

  • The dynamic nature of ECRF function in MT neurons is primarily driven by input stimulus properties.
  • Computational modeling provides a framework for understanding complex neuronal modulation in the visual system.
  • Findings challenge fixed classifications of neuronal receptive field properties, emphasizing stimulus context.