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

Visual System01:26

Visual System

565
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...
565
Vision01:24

Vision

53.1K
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.
53.1K
Parallel Processing01:20

Parallel Processing

150
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...
150
Association Areas of the Cortex01:21

Association Areas of the Cortex

5.2K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
5.2K

You might also read

Related Articles

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

Sort by
Same author

Barcode activity in a recurrent network model of the hippocampus enables efficient memory binding.

eLife·2026
Same author

Dynamics of striatal action selection and reinforcement learning.

eLife·2025
Same author

Selective consolidation of learning and memory via recall-gated plasticity.

eLife·2024
Same author

Dynamics of striatal action selection and reinforcement learning.

bioRxiv : the preprint server for biology·2024
Same author

The connectome of the adult Drosophila mushroom body provides insights into function.

eLife·2020
Same author

Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals.

eLife·2018

Related Experiment Video

Updated: Jun 21, 2025

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

8.3K

Factorized visual representations in the primate visual system and deep neural networks.

Jack W Lindsey1,2, Elias B Issa1,2

  • 1Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States.

Elife
|July 5, 2024
PubMed
Summary
This summary is machine-generated.

Factorization, the separation of scene information, is a key principle in primate visual systems and deep neural networks (DNNs). This strategy enhances object recognition by organizing visual data effectively.

Keywords:
deep neural networksfMRIhumanneurophysiologyneuroscienceobject recognitionrhesus macaquevisual cortexvisual scenes

More Related Videos

The Gateway to the Brain: Dissecting the Primate Eye
07:37

The Gateway to the Brain: Dissecting the Primate Eye

Published on: May 27, 2009

14.1K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

Related Experiment Videos

Last Updated: Jun 21, 2025

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

8.3K
The Gateway to the Brain: Dissecting the Primate Eye
07:37

The Gateway to the Brain: Dissecting the Primate Eye

Published on: May 27, 2009

14.1K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

Area of Science:

  • Neuroscience
  • Computer Vision
  • Computational Neuroscience

Background:

  • Object classification is a primary function of the primate ventral visual stream.
  • Deep neural network (DNN) models are optimized for visual system classification, but may discard or entangle other scene information.
  • Understanding how visual areas represent diverse information beyond object identity is crucial.

Purpose of the Study:

  • To investigate factorization as a normative principle in biological visual representations.
  • To determine if factorization of scene parameters improves object identity decoding.
  • To analyze factorization in DNNs and compare it with neural and behavioral data.

Main Methods:

  • Examined factorization of object pose and background from object identity in monkey ventral visual hierarchy.
  • Assessed the contribution of factorization to object identity decoding performance.
  • Analyzed factorization of scene parameters (lighting, background, viewpoint, pose) in a library of DNN models.
  • Compared DNN performance with neural, fMRI, and behavioral data from humans and monkeys across 12 datasets.

Main Results:

  • Factorization of object pose and background from identity increased in higher visual areas in monkeys.
  • Increased factorization significantly improved object identity decoding in biological vision.
  • DNN models that best matched neural and behavioral data exhibited stronger factorization of scene parameters.
  • Invariance to scene parameters was less consistently associated with matching biological data compared to factorization.

Conclusions:

  • Factorization is a normative principle in biological visual representations.
  • Factorization of visual scene information is a prevalent strategy in both brains and DNNs.
  • Maintaining non-class information in factorized subspaces is often preferred over discarding it (invariance).