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

State Space Representation01:27

State Space Representation

159
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
159
Storage01:23

Storage

61
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
61
Concepts and Prototypes01:24

Concepts and Prototypes

78
The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
78
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

503
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
503
Neural Circuits01:25

Neural Circuits

967
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...
967
The Representativeness Heuristic02:13

The Representativeness Heuristic

15.8K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
15.8K

You might also read

Related Articles

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

Sort by
Same author

Transient boosting of action potential backpropagation for few-shot temporal pattern learning.

PLoS computational biology·2025
Same author

Building on models-a perspective for computational neuroscience.

Cerebral cortex (New York, N.Y. : 1991)·2025
Same author

Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences.

eLife·2025
Same author

Parallel processing of past and future memories through reactivation and synaptic plasticity mechanisms during sleep.

Nature communications·2025
Same author

Single-Trial Representations of Decision-Related Variables by Decomposed Frontal Corticostriatal Ensemble Activity.

eNeuro·2024
Same author

Localizing Syntactic Composition with Left-Corner Recurrent Neural Network Grammars.

Neurobiology of language (Cambridge, Mass.)·2024
Same journal

Costunolide ameliorates autoimmune uveitis by targeting USP15 to suppress TNF-α-induced retinal endothelial inflammation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

A ligandable PNT domain establishes ERG as a directly targetable oncogenic driver in prostate cancer.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Identification of cellular intermediates unveils unique enzymes for flagellar glycan biosynthesis in <i>Clostridioides difficile</i>.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

The structure of correlated variability reflects task-relevant information in sensory neurons.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Shared neurogenetic substrates of nonplanning impulsivity and procrastination.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

HIV-1 capsid interactions with Nuclear Pore Complex components support nuclear entry via affinity gradient.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

Related Experiment Video

Updated: May 23, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

971

A unified neural representation model for spatial and conceptual computations.

Tatsuya Haga1,2, Yohei Oseki3, Tomoki Fukai1

  • 1Neural Computation and Brain Coding Unit, Okinawa Institute of Science and Technology, Onna-son, Okinawa 1919-1, Japan.

Proceedings of the National Academy of Sciences of the United States of America
|March 10, 2025
PubMed
Summary
This summary is machine-generated.

This study reveals a mathematical link between spatial navigation and language processing in the brain. The disentangled successor information (DSI) model bridges these by creating neural representations for both space and concepts.

Keywords:
entorhinal cortexhippocampusnatural language processingspatial navigation

More Related Videos

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.6K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

Related Experiment Videos

Last Updated: May 23, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

971
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.6K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • The hippocampus and entorhinal cortex are crucial for spatial memory, utilizing place cells and grid cells.
  • These same brain regions also represent abstract semantic concepts (concept cells).
  • A potential link between spatial and semantic computation mechanisms in the brain is suggested but not fully understood.

Purpose of the Study:

  • To investigate the relationship between neurocomputational mechanisms for spatial knowledge and semantic concepts.
  • To propose a unified neural representation model integrating spatial and semantic computations.
  • To explore the biological plausibility and computational inferences of the proposed model.

Main Methods:

  • Developed a mathematical correspondence between spatial navigation value functions and natural language processing word embedding information measures.
  • Integrated spatial and semantic computations into a novel neural representation model: disentangled successor information (DSI).
  • Utilized DSI to generate biologically plausible spatial (place/grid cells) and semantic (concept cells) representations.

Main Results:

  • The DSI model successfully generates neural representations akin to place cells, grid cells, and concept cells.
  • DSI enables common computational framework for inferring spatial contexts and words using simple arithmetic operations.
  • The model's computations are biologically interpretable through partial modulations of cell assemblies.

Conclusions:

  • Established a theoretical connection between spatial and semantic computations in the brain.
  • The DSI model provides a unified framework for understanding hippocampal and entorhinal cortex functions.
  • Suggests novel computational roles for neural representations in spatial navigation and concept processing.