Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Anatomy of the Brain: Major Regions

Anatomy of the Brain: Major Regions

The brain is the most complex organ in the human body. It consists of four main parts: the cerebrum, diencephalon, cerebellum, and brainstem.
The cerebrum is the largest section of the brain and divides into left and right hemispheres, separated by a deep fissure. The cerebral outer layer of grey matter — the cerebral cortex — comprises elevations called gyri and shallow groves called sulci. The inner portion of white matter includes long nerve fibers known as axons, which connect various areas...

Motor and Sensory Areas of the Cortex

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.

Higher Mental Functions of the Brain: Language

Higher Mental Functions of the Brain: Language

Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...

Methods of Obtaining Topography

Methods of Obtaining Topography

Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...

You might also read

Related Articles

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

Sort by

Same author

Category selectivity observed in the human brain is distinct from category selectivity observed in artificial neural networks.

bioRxiv : the preprint server for biology·2026

Same author

Can Category-selective Cortex Predict Categorization Behavior?

Journal of cognitive neuroscience·2026

Same author

Individual differences in artificial neural networks capture individual differences in human behavior.

bioRxiv : the preprint server for biology·2026

Same author

MOSAIC: A scalable framework for fMRI dataset aggregation and modeling of human vision.

bioRxiv : the preprint server for biology·2026

Same author

Human-like individual differences emerge from random weight initializations in neural networks.

bioRxiv : the preprint server for biology·2025

Same author

Enhancement of catalytic activity using CoFeLDH for the formation of biologically active diaryl sulfide and propargylamine derivatives: molecular docking, DFT, dynamics, and ADMET analyses of their biocidal and anti-diabetic activities.

RSC advances·2025

Search research articles

Related Experiment Video

Updated: May 8, 2026

Revealing Neural Circuit Topography in Multi-Color

Revealing Neural Circuit Topography in Multi-Color

Published on: November 14, 2011

TopoNets: High performing vision and language models with brain-like topography.

Mayukh Deb^1,2, Mainak Deb³, N Apurva Ratan Murty^1,2

¹Cognition and Brain Science, School of Psychology, Georgia Tech.

... International Conference on Learning Representations

|March 9, 2026

Summary

This study introduces TopoLoss, a novel AI training method that organizes artificial neural networks spatially, mimicking brain structure without performance loss. TopoNets achieve top performance and exhibit brain-like efficiencies.

More Related Videos

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Related Experiment Videos

Last Updated: May 8, 2026

Revealing Neural Circuit Topography in Multi-Color

Revealing Neural Circuit Topography in Multi-Color

Published on: November 14, 2011

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Artificial Intelligence
Computational Neuroscience
Machine Learning

Background:

Biological neurons exhibit functional organization, with nearby cells sharing similar roles.
Current AI models lack this inherent spatial organization, often sacrificing performance for topographic structure.

Purpose of the Study:

To develop a method for creating spatially organized topographic representations in AI models.
To achieve brain-like organization in AI without compromising task performance.

Main Methods:

Introduction of TopoLoss, a new loss function designed to promote spatial topography in AI.
Integration of TopoLoss into leading AI architectures including ResNet, ViT, GPT-Neo, and NanoGPT, creating TopoNets.
Validation across vision and language models.

Main Results:

TopoNets demonstrate superior performance among supervised topographic models.
TopoNets exhibit brain-like properties: localized feature processing, reduced dimensionality, and enhanced efficiency.
TopoNets successfully predict neural responses and replicate topographic signatures in the brain's visual and language cortices.

Conclusions:

TopoLoss provides a robust and adaptable framework for integrating topography into AI models.
TopoNets represent a significant advancement in creating high-performing AI that emulates human brain computational strategies.
This work bridges the gap between biological and artificial systems by creating AI with brain-like topographic organization.