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 Experiment Videos

Deforming the hippocampal map.

David S Touretzky1, Wendy E Weisman, Mark C Fuhs

  • 1Computer Science Department and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3891, USA. dst@cs.cmu.edu

Hippocampus
|September 25, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Persistently Increased Expression of PKMzeta and Unbiased Gene Expression Profiles Identify Hippocampal Molecular Traces of a Long-Term Active Place Avoidance Memory and "Shadow" Proteins.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Temporal Coding rather than Circuit Wiring allows Hippocampal CA3 Neurons to Dynamically Distinguish Different Cortical Inputs.

bioRxiv : the preprint server for biology·2026
Same author

Active locomotion predictively rescues head direction attractor dynamics in head-fixed mice.

bioRxiv : the preprint server for biology·2026
Same author

Overdispersion: Navigating Noise, Learning and Remembering.

Hippocampus·2025
Same author

Optogenetic stimulation of memory-tagged neurons elicits endogenous patterns of neural activity.

bioRxiv : the preprint server for biology·2025
Same author

Effective computations for hippocampal place cell phenomena in sparse untrained random networks.

bioRxiv : the preprint server for biology·2025
Same journal

Opioid-Associated Hippocampal Injury: Past, Present, and Future Directions.

Hippocampus·2026
Same journal

Neural and Navigational Features Influencing the Novelty Induced Benefits on Episodic Memory.

Hippocampus·2026
Same journal

Intrinsic Persistent Firing in CA1 Encodes Elapsed Time Across Behaviorally Relevant Scales.

Hippocampus·2026
Same journal

Boundary Vector Cells Encode a Future-Biased Spectrum of Positions in the Rat.

Hippocampus·2026
Same journal

Hippocampal NOP Receptor Activation Impairs Object Recognition Memory Acquisition.

Hippocampus·2026
Same journal

Effects of Corticotropin-Releasing Factor 1 Receptor Antagonism on In Vivo Dentate Gyrus Long-Term Potentiation in the TgF344-AD Rat Model of Alzheimer's Disease.

Hippocampus·2026
See all related articles

This study explores how neural networks model rat navigation. Attractor neural networks can replicate how rats use landmarks to form cognitive maps, aligning with maximum likelihood estimations.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Place cells in the hippocampus are crucial for spatial navigation.
  • Conjoint stimulus control describes how multiple environmental cues influence neural activity.
  • Previous work by Fenton et al. observed deformations in the cognitive map of rats based on landmark rotations.

Purpose of the Study:

  • To investigate neural network mechanisms that can account for conjoint stimulus control over place cells.
  • To model the deformation of cognitive maps observed in rats foraging in environments with rotating cues.
  • To compare attractor neural network models with maximum likelihood formulations for spatial estimation.

Main Methods:

  • Utilized an attractor neural network model with recurrent connections to simulate neural activity.

Related Experiment Videos

  • Formulated an abstract, maximum likelihood approach to estimate rat location based on landmark positions.
  • Compared the outputs of the neural network model with the maximum likelihood model under simulated landmark rotation conditions.

    • Main Results:

    • Attractor neural networks can generate activity patterns that mimic the observed shifts in place cell firing fields.
    • The neural network model's performance is comparable to maximum likelihood estimations when landmark features are appropriately selected.
    • Recurrent neural networks demonstrate the capacity to efficiently implement maximum likelihood computations for spatial tasks.

    Conclusions:

    • Attractor neural networks provide a plausible neural mechanism for conjoint stimulus control in spatial navigation.
    • The findings support the idea that neural networks can implement sophisticated probabilistic computations for cognitive functions.
    • This research bridges computational modeling and experimental observations of spatial memory and navigation.