Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Convolution: Math, Graphics, and Discrete Signals

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

The Role of Ion Channels in Neuronal Computation

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.

You might also read

Related Articles

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

Sort by

Same author

Computational modeling for precision targeting of conductivity-clamped gene electrotransfer within the striatum of the human brain.

Journal of neural engineering·2026

Same author

Investigating the Biomechanics to Mimic Blinking using an Electromagnetic Actuator.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

Prototype Hardware for Closed-loop Stimulation of the Retina.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

Modelling of cardiac biventricular electromechanics with coronary blood flow to investigate the influence of coronary arterial motion on coronary haemodynamic.

Computer methods and programs in biomedicine·2025

Same author

Development of a Low-Cost Electrospinner for Fabrication of Polyacrylonitrile Fibers as a Precursor to Flexible Carbon Neural Electrodes.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

Percutaneous Connector for Large Animal, in vivo Studies, with Open-Source Designs.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Search research articles

Related Experiment Video

Updated: May 14, 2026

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

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

Published on: June 21, 2022

Convolution based method for calculating inputs from dendritic fields in a continuum model of the retina.

Amr Al Abed¹, Shijie Yin, Gregg J Suaning

¹Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|February 1, 2013

Summary

Computational models help design prosthetic vision. A new method calculates retinal ganglion cell (RGC) activation by electrical stimulation, aiding device development.

More Related Videos

Implementing Dynamic Clamp with Synaptic and Artificial Conductances in Mouse Retinal Ganglion Cells

Implementing Dynamic Clamp with Synaptic and Artificial Conductances in Mouse Retinal Ganglion Cells

Published on: May 16, 2013

Analyzing Dendritic Morphology in Columns and Layers

Analyzing Dendritic Morphology in Columns and Layers

Published on: March 23, 2017

Related Experiment Videos

Last Updated: May 14, 2026

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

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

Published on: June 21, 2022

Implementing Dynamic Clamp with Synaptic and Artificial Conductances in Mouse Retinal Ganglion Cells

Implementing Dynamic Clamp with Synaptic and Artificial Conductances in Mouse Retinal Ganglion Cells

Published on: May 16, 2013

Analyzing Dendritic Morphology in Columns and Layers

Analyzing Dendritic Morphology in Columns and Layers

Published on: March 23, 2017

Area of Science:

Computational neuroscience
Biomedical engineering
Retinal electrophysiology

Background:

Electrical stimulation is key for prosthetic vision.
Understanding neuron activation is crucial for device efficacy.
Continuum models simplify retinal electrophysiology.

Purpose of the Study:

To present a convolution-based method for calculating effective extracellular loading in retinal ganglion cells (RGCs).
To assess the impact of dendritic tree size on RGC activation by electrical stimulation.
To refine computational models for prosthetic vision device design.

Main Methods:

Developed a convolution-based method to compute local spatial average of effective extracellular loading.
Utilized a continuum model of retinal electrophysiology with an active RGC tissue layer.
Simulated electrical stimulation using a hexagonal arrangement of electrodes (hexpolar) in the suprachoroidal space.

Main Results:

The method accurately calculates extracellular loading in RGCs.
Demonstrated the influence of dendritic tree size on RGC activation thresholds.
Provided a tool to optimize stimulation strategies for prosthetic vision.

Conclusions:

The presented method enhances computational modeling of retinal stimulation.
Dendritic tree morphology significantly affects RGC activation.
This work contributes to the development of more effective prosthetic vision systems.