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

Storage01:23

Storage

134
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...
134
Somatosensation01:33

Somatosensation

38.5K
The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.
38.5K
Motor and Sensory Areas of the Cortex01:14

Motor and Sensory Areas of the Cortex

4.6K
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....
4.6K

You might also read

Related Articles

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

Sort by
Same author

Strong strain dependence of friction in graphene kirigami allows engineering a negative coefficient of friction.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

An Extended Energy-Biased Aggregation-Volume-Bias Monte Carlo (EB-AVBMC) Method for Nucleation Simulation of a Reactive Water Potential.

Journal of chemical theory and computation·2025
Same author

Hexagons all the way down: grid cells as a conformal isometric map of space.

PLoS computational biology·2025
Same author

Genetic Algorithm Workflow for Parameterization of a Water Model Using the Vashishta Force Field.

The journal of physical chemistry. B·2025
Same author

Inferring causal connectivity from pairwise recordings and optogenetics.

PLoS computational biology·2023
Same author

Optogenetic pacing of medial septum parvalbumin-positive cells disrupts temporal but not spatial firing in grid cells.

Science advances·2021
Same journal

Distinct involvements of the subthalamic nucleus subpopulations in reward-biased decision-making in monkeys.

eLife·2026
Same journal

Pink1-mediated mitophagy in the endothelium releases proteins encoded by mitochondrial DNA and activates neutrophil responses during inflammation.

eLife·2026
Same journal

Restraint of melanoma progression by cells in the local skin environment.

eLife·2026
Same journal

Brawn before bite in endemic Asian eutherian mammals after the end-Cretaceous extinction.

eLife·2026
Same journal

Experimental evolution to thermal stress indicates climate resilience in a cosmopolitan arthropod.

eLife·2026
Same journal

Correlates of protection against African swine fever virus identified by a systems immunology approach.

eLife·2026
See all related articles

Related Experiment Video

Updated: Sep 13, 2025

Implantation of Chronic Silicon Probes and Recording of Hippocampal Place Cells in an Enriched Treadmill Apparatus
09:59

Implantation of Chronic Silicon Probes and Recording of Hippocampal Place Cells in an Enriched Treadmill Apparatus

Published on: October 11, 2017

12.8K

Learning place cells and remapping by decoding the cognitive map.

Markus Borud Pettersen1,2, Vemund Schøyen2, Anders Malthe-Sørenssen3

  • 1Simula Research Laboratory, Oslo, Norway.

Elife
|July 28, 2025
PubMed
Summary
This summary is machine-generated.

This study models hippocampal place cells, showing how neural networks can learn spatial representations and remapping, potentially explaining interactions with border and grid cells during navigation.

Keywords:
artificial intelligenceborder cellsmachine learningneurosciencenoneplace cellsrecurrent neural networkspatial navigation

More Related Videos

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents
04:41

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents

Published on: December 2, 2022

2.9K
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.2K

Related Experiment Videos

Last Updated: Sep 13, 2025

Implantation of Chronic Silicon Probes and Recording of Hippocampal Place Cells in an Enriched Treadmill Apparatus
09:59

Implantation of Chronic Silicon Probes and Recording of Hippocampal Place Cells in an Enriched Treadmill Apparatus

Published on: October 11, 2017

12.8K
Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents
04:41

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents

Published on: December 2, 2022

2.9K
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.2K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Hippocampal place cells encode location and form cognitive maps.
  • Place cells exhibit remapping (rate changes) when environments change.
  • Interactions between place, border, and grid cells remain unclear.

Purpose of the Study:

  • To develop a normative computational model of place cell function and remapping.
  • To investigate how neural networks can learn spatial representations and perform path integration.
  • To explore potential mechanisms for interaction between different types of spatially tuned neurons.

Main Methods:

  • A neural network model was developed for position reconstruction and path integration.
  • A non-trainable decoding scheme was used to estimate position from network outputs.
  • The model was trained in multiple simulated environments to observe remapping phenomena.

Main Results:

  • Network output units developed place-like spatial representations.
  • Upstream recurrent units became boundary-tuned.
  • Place-like units demonstrated global, geometric, and rate remapping similar to biological cells.
  • Place unit centers showed hexagonal lattice clustering, with preliminary evidence in mouse data.
  • Remapping was supported by rate changes in upstream units.

Conclusions:

  • The model provides a normative framework for understanding place cell field formation and remapping.
  • The findings suggest a potential mechanism for interaction between place, border, and grid cells.
  • This work offers new insights into the computational principles underlying spatial cognition.