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

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...
Vision01:24

Vision

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.
Parallel Processing01:20

Parallel Processing

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...
Visual Agnosia01:12

Visual Agnosia

Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round end"...
Association Areas of the Cortex01:21

Association Areas of the Cortex

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,...
Motor and Sensory Areas of the Cortex01:14

Motor and Sensory Areas of the Cortex

The cerebral cortex, the brain's outermost layer, is pivotal in processing complex cognitive tasks, emotions, and various sensory inputs and executing voluntary motor activities. This intricate structure is divided into three primary functional areas: the motor areas, sensory areas, and association areas.
Motor Areas
The motor areas located in the frontal lobe are central to controlling voluntary movements. This region is further subdivided into the primary motor cortex and the premotor cortex.

You might also read

Related Articles

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

Sort by
Same author

Predisposed and learned preferences for multipoint visual statistics in visually naive newly hatched chicks.

Proceedings. Biological sciences·2026
Same author

Neural representations of visual memory in inferotemporal cortex reveal a generalizable framework for translating between spikes and field potentials.

bioRxiv : the preprint server for biology·2026
Same author

Sharpened Visual Memory Representations Are Reflected in Inferotemporal Cortex.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2025
Same author

Seeing what you hear: Compression of rat visual perceptual space by task-irrelevant sounds.

PLoS computational biology·2025
Same author

The unexpected value of communicating science to the public.

Nature reviews. Neuroscience·2025
Same author

Resolving a paradox about how vision is transformed into familiarity.

bioRxiv : the preprint server for biology·2025
Same journal

Sparse component analysis: A method that uncovers separable computations within neural population activity.

Neuron·2026
Same journal

Spatiomolecular mapping reveals anatomical organization of heterogeneous cell types in the human nucleus accumbens.

Neuron·2026
Same journal

TGF-β1-induced endothelial transcytosis drives blood-brain barrier leakage during aging.

Neuron·2026
Same journal

Image space opens up for visual neuroscience.

Neuron·2026
Same journal

Septal GLP-1 receptors control alcohol taking and seeking.

Neuron·2026
Same journal

Microglial fitness in moderation: Tuning TREM2 signaling through Ptpn6.

Neuron·2026
See all related articles

Related Experiment Video

Updated: May 25, 2026

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

How does the brain solve visual object recognition?

James J DiCarlo1, Davide Zoccolan, Nicole C Rust

  • 1Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. dicarlo@mit.edu

Neuron
|February 14, 2012
PubMed
Summary
This summary is machine-generated.

Understanding how the brain rapidly recognizes objects is key. This review proposes a new approach using computational models and brain data to uncover the underlying neural algorithms for core object recognition.

More Related Videos

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Related Experiment Videos

Last Updated: May 25, 2026

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

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • The brain's ability to perform core object recognition, identifying objects despite variations in appearance, is crucial for daily function.
  • Evidence suggests this process involves a feedforward computational cascade culminating in the inferior temporal cortex.
  • The specific algorithm driving this recognition remains largely unknown.

Purpose of the Study:

  • To review current evidence on the neural basis of core object recognition.
  • To propose a framework for understanding the computational algorithm underlying this ability.
  • To bridge the gap between neuronal, behavioral, and computational models of object recognition.

Main Methods:

  • Review of existing literature on neuronal recordings, psychophysical data, and computational models.
  • Analysis of evidence from individual neurons, neuronal populations, and behavior.
  • Synthesis of findings to evaluate various computational models.

Main Results:

  • Core object recognition relies on a feedforward cascade of computations.
  • The inferior temporal cortex plays a critical role in generating robust object representations.
  • Current understanding of the underlying algorithm is limited.

Conclusions:

  • Understanding the core object recognition algorithm requires integrating diverse data sources.
  • A proposed approach involves using neuronal and psychophysical data to test computational models.
  • Future research should focus on canonical subnetworks with specific functional goals to elucidate the recognition algorithm.