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 Experiment Videos

Cluster method for analysis of transmitted information in multivariate neuronal data

M N Chee-Orts1, L M Optican

  • 1Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD 20892.

Biological Cybernetics
|January 1, 1993
PubMed
Summary

A novel method quantifies transmitted information in high-dimensional neuronal data using cluster formation. This computationally efficient technique reveals more about stimulus patterns than color in VI neuron responses.

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

Preface.

Progress in brain research·2022
Same author

EYE MOVEMENT RECORDING AND NONLINEAR DYNAMICS ANALYSIS - THE CASE OF SACCADES.

Romanian journal of biophysics·2015
Same author

DYNAMICAL COMPLEXITY ANALYSIS OF SACCADIC EYE MOVEMENTS IN TWO DIFFERENT PSYCHOLOGICAL CONDITIONS.

Romanian reports in physics·2015
Same author

Applying saccade models to account for oscillations.

Progress in brain research·2008
Same author

Suppression of saccadic intrusions in hereditary ataxia by memantine.

Neurology·2008
Same author

Irregularity distinguishes limb tremor in cervical dystonia from essential tremor.

Journal of neurology, neurosurgery, and psychiatry·2007

Area of Science:

  • Computational neuroscience
  • Information theory
  • Machine learning

Background:

  • Quantifying information transmission in complex neuronal systems is challenging.
  • High-dimensional data from neural recordings requires efficient analytical methods.
  • Understanding how neurons encode stimulus features is crucial for neuroscience.

Purpose of the Study:

  • To introduce a new method for quantifying transmitted information and channel capacity in high-dimensional data.
  • To assess the computational efficiency of the proposed method.
  • To apply the method to neuronal data and compare information transmission about different stimulus features.

Main Methods:

  • Developed a novel quantification method based on cluster formation.
  • Applied the method to high-dimensional neuronal data, specifically responses of a VI neuron.

Related Experiment Videos

  • Evaluated computational efficiency in terms of processing time and memory usage.
  • Main Results:

    • The method successfully quantifies transmitted information and channel capacity for high-dimensional data.
    • The approach is computationally efficient, requiring minimal processing time and memory.
    • Analysis of VI neuron responses indicated greater information transmission about stimulus patterns compared to color.

    Conclusions:

    • The cluster-based method provides a complete and efficient way to measure information in neuronal data.
    • This technique advances the analysis of high-dimensional neural information.
    • Neuronal encoding prioritizes stimulus pattern information over color information in the studied VI neuron.