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

Partial cross-correlation analysis resolves ambiguity in the encoding of multiple movement features.

Eran Stark1, Rotem Drori, Moshe Abeles

  • 1Dept. of Physiology, Hadassah Medical School, The Hebrew University of Jerusalem, Jerusalem 91120, Israel. eranstark@md.huji.ac.il

Journal of Neurophysiology
|December 2, 2005
PubMed
Summary
This summary is machine-generated.

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

Social remapping of spatial coding along the dorsoventral axis of the mouse prefrontal cortex.

Cell reports·2025
Same author

Converging assemblies: A putative building block for brain function and for interfacing with the brain.

Neural regeneration research·2025
Same author

The generation of a neuronal phase code for space.

Clinical and translational medicine·2024
Same author

Learning to learn: Single session acquisition of new rules by freely moving mice.

PNAS nexus·2024
Same author

Local activation of CA1 pyramidal cells induces theta-phase precession.

Science (New York, N.Y.)·2024
Same author

Wired together, change together: Spike timing modifies transmission in converging assemblies.

Science advances·2024
Same journal

Targeting intracranial electrical stimulation to network regions defined within individuals causes network-level effects.

Journal of neurophysiology·2026
Same journal

When "Noise" Isn't Simply Noise: Deterministic Postural Drive During Noisy Galvanic Vestibular Stimulation (nGVS).

Journal of neurophysiology·2026
Same journal

Abrupt Scene Onsets and Gradually Emerging Scene Information Produce Distinct EEG Decoding Dynamics.

Journal of neurophysiology·2026
Same journal

From discovery to translation: charting a course for the <i>Journal of Neurophysiology</i>.

Journal of neurophysiology·2026
Same journal

Neuromodulatory Strategies Overcome Multiple Inevitable Impairments of Cerebral Palsy.

Journal of neurophysiology·2026
Same journal

Acute Fentanyl Toxicity:From Opioid-Induced to Hypoxia-Mediated Pathophysiology.

Journal of neurophysiology·2026
See all related articles

This study introduces a new statistical method to precisely identify which stimulus or action features are encoded in neural activity, even with complex interdependencies. The approach clarifies ambiguous brain representations by analyzing correlations across multiple time delays.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Understanding neural representations of stimuli and actions is a core neuroscience challenge.
  • Interdependent features in stimuli or actions create ambiguity in neural activity analysis.
  • Existing methods struggle to account for delayed interdependencies between neural activity and features.

Purpose of the Study:

  • To resolve ambiguity in neural encoding caused by interdependent features.
  • To develop a statistical method capable of analyzing delayed interdependencies.
  • To provide a scalable and comparable output for neural activity analysis.

Main Methods:

  • Application of a novel statistical method based on partial cross-correlations.
  • Estimating linear correlations between neural activity and specific features.

Related Experiment Videos

  • Accounting for linear correlations with other features at multiple time delays.

    • Main Results:

    • The method effectively resolves ambiguity in neural encoding.
    • It provides estimates of feature-specific neural correlations unaffected by confounding factors.
    • Graphical output allows for standardized comparisons across features, neurons, and experiments.

    Conclusions:

    • The novel statistical method accurately identifies feature-specific neural representations.
    • It successfully handles complex interdependencies and delayed relationships.
    • This approach enhances the understanding of how the brain encodes information.