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

Using response models to study coding strategies in monkey visual cortex

M C Wiener1, B J Richmond

  • 1Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892-4415, USA.

Bio Systems
|January 14, 1999
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

Resisting the Urge to Act: DREADDS Modifying Habits: (Trends in Neurosciences 40, 61-62; 2017).

Trends in neurosciences·2017
Same author

Injection parameters and virus dependent choice of promoters to improve neuron targeting in the nonhuman primate brain.

Gene therapy·2014
Same author

Excess synchrony in motor cortical neurons provides redundant direction information with that from coarse temporal measures.

Journal of neurophysiology·2001
Same author

Consistency of encoding in monkey visual cortex.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2001
Same author

Geometric visual hallucinations, Euclidean symmetry and the functional architecture of striate cortex.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2001
Same author

Role of perirhinal cortex in object perception, memory, and associations.

Current opinion in neurobiology·2001
Same journal

Ruliological Resilience: Pattern Restoration and Robustness in Wolfram Patterns. A Basis for Regeneration, Not Just in Cone Shells?

Bio Systems·2026
Same journal

The quantum-to-classical transducer: A thermodynamic and quantum mechanical framework for the emergence of bioenergetics.

Bio Systems·2026
Same journal

Forward-backward gene expression binarization for boolean state inference over a known regulatory network.

Bio Systems·2026
Same journal

Partial-label metric ceilings for evaluating gene regulatory networks inferred from single-cell foundation models.

Bio Systems·2026
Same journal

The impedance mismatch theory: A non-equilibrium thermodynamic framework for a shared energetic stress pathway in neurodegeneration.

Bio Systems·2026
Same journal

Immune signal-status misclassification: A theoretical framework for biological status assignment and failed status resolution.

Bio Systems·2026
See all related articles

This study introduces a new method for estimating transmitted information using an explicit model of neural response strength and variability. This approach accurately calculates channel capacity, revealing a trade-off between dynamic range and signal variance in neural coding.

Area of Science:

  • Computational Neuroscience
  • Information Theory
  • Neurobiology

Background:

  • Estimating transmitted information typically relies on direct measurement of empirical distributions.
  • This method can be limited by the specific stimulus sets used in experiments.
  • Understanding neural coding requires quantifying information transmission and its relationship to neural variability.

Purpose of the Study:

  • To develop and validate a novel method for estimating transmitted information using an explicit model.
  • To accurately calculate neuronal channel capacity, representing the maximum transmittable information.
  • To investigate the relationship between dynamic range, response variability, and channel capacity in different visual cortical areas.

Main Methods:

  • Developed an explicit model linking neural response strength (spike count) to its variability.

Related Experiment Videos

  • Used this model to estimate conditional probabilities for calculating transmitted information.
  • Calculated channel capacity, assuming the model characterizes all possible response distributions.

    • Main Results:

    • The explicit model provides accurate estimates of transmitted information, reliable for datasets with nine or more trials.
    • Channel capacity is uniquely defined by the model, independent of the specific stimulus set.
    • Channel capacity increases with dynamic range and decreases with increased signal variance; neurons in V1 and IT exhibit similar channel capacities despite differences in dynamic range and variance.

    Conclusions:

    • An explicit model of neural response properties offers a robust method for estimating transmitted information and channel capacity.
    • The findings suggest a fundamental trade-off between dynamic range and signal variance in neural coding across different brain regions.
    • This approach facilitates a more accurate and generalizable understanding of information processing in the nervous system.