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

Propagation of Action Potentials01:25

Propagation of Action Potentials

4.8K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium...
4.8K
Vision01:24

Vision

52.4K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
52.4K
Visual System01:26

Visual System

438
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
438
Color Vision01:24

Color Vision

398
Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
398
Neural Circuits01:25

Neural Circuits

938
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
938
Parallel Processing01:20

Parallel Processing

131
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
131

You might also read

Related Articles

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

Sort by
Same author

Contrast and pattern adaptation in visual cortex share a common gain control mechanism.

Journal of neurophysiology·2026
Same author

Contrast and pattern adaptation in visual cortex share a common gain control mechanism.

bioRxiv : the preprint server for biology·2025
Same author

Establishing a continuum of cell types in the visual cortex.

bioRxiv : the preprint server for biology·2025
Same author

A universal power law optimizes energy and representation fidelity in visual adaptation.

bioRxiv : the preprint server for biology·2025
Same author

Adaptation shapes the representational geometry in mouse V1 to efficiently encode the environment.

bioRxiv : the preprint server for biology·2025
Same author

Temporal dynamics of energy-efficient coding in mouse primary visual cortex.

bioRxiv : the preprint server for biology·2025
Same journal

Comprehensive Analysis of Auditory Nerve Fiber Responses using Fiber-Specific Modeling.

Journal of neurophysiology·2026
Same journal

HCN channels modulate the medium afterhyperpolarization and adjust the firing gain of fast alpha motoneurons in mice.

Journal of neurophysiology·2026
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
See all related articles

Related Experiment Video

Updated: May 14, 2025

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns
09:42

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

5.9K

Dynamics of energy-efficient coding in visual cortex.

S Amin Moosavi1, Antonia Pastor1, Alfredo G Ornelas2

  • 1Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, California, United States.

Journal of Neurophysiology
|May 5, 2025
PubMed
Summary
This summary is machine-generated.

Sensory input representation becomes more efficient over time. Initially broad activation refines to sparser, more informative neural activity, optimizing coding efficiency.

Keywords:
cortical popualtionsdynamicsefficient codingmutual informationsparse coding

More Related Videos

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.3K
Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging
11:24

Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging

Published on: December 12, 2012

13.6K

Related Experiment Videos

Last Updated: May 14, 2025

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns
09:42

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

5.9K
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.3K
Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging
11:24

Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging

Published on: December 12, 2012

13.6K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Sensory Coding

Background:

  • Sparse coding is a principle for efficient neural representation of sensory information.
  • The temporal dynamics of sparse coding in cortical populations are not well understood.

Purpose of the Study:

  • To investigate the temporal dynamics of sparse coding in cortical populations during sensory input processing.
  • To understand how coding efficiency changes over time with stimulus presentation.

Main Methods:

  • Analysis of population neural activity in response to stimulus onset.
  • Quantification of sparseness, mutual information, and metabolic cost.
  • Examination of competitive interactions within neural populations.

Main Results:

  • Stimulus onset initially causes broad cortical activation, decreasing sparseness and increasing mutual information.
  • Over time, neural activity refines, maintaining high mutual information as sparseness increases and activity decreases.
  • Coding efficiency, defined as mutual information per metabolic cost, consistently improves during stimulus presentation.

Conclusions:

  • Cortical sensory representations are actively optimized over time.
  • Temporal dynamics involve an initial broad activation followed by refinement through competitive interactions.
  • The brain dynamically adjusts neural population activity to enhance coding efficiency.