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

Parallel Processing01:20

Parallel Processing

252
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...
252
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.3K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
3.3K
Visual System01:26

Visual System

706
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...
706
Neural Circuits01:25

Neural Circuits

1.6K
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...
1.6K
Vision01:24

Vision

55.4K
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.
55.4K
Somatosensory, Motor, and Association Cortex01:24

Somatosensory, Motor, and Association Cortex

1.0K
The somatosensory cortex in the parietal lobes is crucial for interpreting sensory data such as touch, temperature, and proprioception. The somatosensory cortex, situated in the parietal lobes, plays a vital role in interpreting sensory information like touch, temperature, and proprioception—awareness of body position. This specialized brain region features an organized structure wherein neurons at the top primarily process sensations originating from the lower body. In contrast, those at...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Parsing auditory neural code into maximum-entropy packets.

bioRxiv : the preprint server for biology·2025
Same author

Hippocampal spatial representations exhibit a hyperbolic geometry that expands with experience.

Nature neuroscience·2022
Same author

Hyperbolic geometry of the olfactory space.

Science advances·2018
Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Sep 18, 2025

Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice
09:28

Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice

Published on: June 23, 2023

3.1K

Computations that sustain neural feature selectivity across processing stages.

Ryan J Rowekamp1, Tatyana O Sharpee1,2

  • 1Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, United States of America.

Plos Computational Biology
|June 20, 2025
PubMed
Summary
This summary is machine-generated.

The brain uses coordinated excitatory and suppressive neural computations to precisely recognize objects. This nonlinear mechanism enhances visual processing and selectivity for natural stimuli.

More Related Videos

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
09:37

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Published on: July 5, 2015

9.2K
Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
07:08

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

8.4K

Related Experiment Videos

Last Updated: Sep 18, 2025

Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice
09:28

Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice

Published on: June 23, 2023

3.1K
Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
09:37

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Published on: July 5, 2015

9.2K
Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
07:08

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

8.4K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Visual Processing

Background:

  • Biological visual systems excel at object recognition, but the underlying neural mechanisms are not fully understood.
  • Understanding neural computations is key to deciphering visual perception.

Purpose of the Study:

  • Investigate neural responses in visual areas V1, V2, and V4 to natural stimuli.
  • Explore the role of quadratic computations and local recurrent interactions in visual processing.

Main Methods:

  • Utilized a computational framework incorporating quadratic computations to model neural responses.
  • Analyzed neural activity in response to natural visual stimuli.
  • Focused on capturing both excitatory and suppressive local recurrent interactions.

Main Results:

  • Quadratic computations significantly improved model predictive power and neural selectivity.
  • Coordination between excitatory and suppressive features was crucial, especially in V4.
  • A balance between excitatory and suppressive components was maintained across processing stages.
  • Feature selectivity was refined from broader representations in earlier stages to more specific ones in later stages.

Conclusions:

  • The brain employs multiple nonlinear mechanisms, including coordinated quadratic computations, to enhance neural selectivity for natural stimuli.
  • These mechanisms allow for the representation of complex and mutually exclusive stimulus features.
  • This study provides insights into how the visual system achieves robust object recognition.