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

You might also read

Related Articles

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

Sort by
Same author

Flexible Gravitational-Wave Parameter Estimation with Transformers.

Physical review letters·2026
Same author

Neural timescales from a computational perspective.

Nature neuroscience·2026
Same author

M2Viz: a tool for visualizing genetic or proteomic modifications and variants.

BMC bioinformatics·2026
Same author

Differential phosphorylation of PHIP phosphopeptides with implications in insulin signaling.

BMC molecular and cell biology·2026
Same author

Corrigendum to "Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes" [Medical Image Analysis 103 (2025) 103580].

Medical image analysis·2026
Same author

Adaptation of visual responses in degenerating <i>rd10</i> and healthy mouse retinas during ongoing electrical stimulation.

Frontiers in neuroscience·2026

Related Experiment Video

Updated: Dec 20, 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

Characterizing Retinal Ganglion Cell Responses to Electrical Stimulation Using Generalized Linear Models.

Sudarshan Sekhar1,2,3,4,5, Poornima Ramesh6, Giacomo Bassetto6,7

  • 1Institute for Ophthalmic Research, Eberhard Karls University Tübingen, Tübingen, Germany.

Frontiers in Neuroscience
|June 2, 2020
PubMed
Summary

This study develops a statistical framework to analyze how retinal ganglion cells (RGCs) respond to electrical stimulation. Understanding these responses is key for creating better visual prosthetics by optimizing stimulation parameters.

Keywords:
SNRgeneralized linear modelsnested modelsprostheticsretinawhite-noise stimulation

More Related Videos

An Isolated Retinal Preparation to Record Light Response from Genetically Labeled Retinal Ganglion Cells
13:02

An Isolated Retinal Preparation to Record Light Response from Genetically Labeled Retinal Ganglion Cells

Published on: January 26, 2011

17.2K
Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats
10:30

Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats

Published on: July 1, 2016

12.8K

Related Experiment Videos

Last Updated: Dec 20, 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
An Isolated Retinal Preparation to Record Light Response from Genetically Labeled Retinal Ganglion Cells
13:02

An Isolated Retinal Preparation to Record Light Response from Genetically Labeled Retinal Ganglion Cells

Published on: January 26, 2011

17.2K
Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats
10:30

Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats

Published on: July 1, 2016

12.8K

Area of Science:

  • Neuroscience
  • Biophysics
  • Visual Prosthetics Research

Background:

  • Improving visual prosthetics requires preferential stimulation of distinct retinal pathways.
  • Retinal ganglion cells (RGCs) exhibit unique linear electrical input filters under low-amplitude white noise stimulation.

Purpose of the Study:

  • To establish a statistical framework for characterizing RGC responses to white-noise electrical stimulation.
  • To objectively quantify stimulus paradigms based on elicited neural response types (linear, non-linear, stimulus-independent).

Main Methods:

  • Utilized a nested family of Generalized Linear Models (GLMs).
  • Partitioned neural responses by progressively adding covariates for non-stationarity, linear stimulus dependence, and non-linear interactions.

Main Results:

  • Each added model component improved performance in predicting RGC responses.
  • Even complex non-linear models left a significant portion of neural variability unexplained.

Conclusions:

  • The developed framework aids in optimizing stimulus parameters for visual prosthetics.
  • This research helps avoid issues like indiscriminate retinal activation and low signal-to-noise ratio responses.