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

Attention, configuration, and hippocampal function

C V Buhusi1, N A Schmajuk

  • 1Department of Psychology, Experimental, Duke University, Durham, North Carolina 27706, USA.

Hippocampus
|January 1, 1996
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

Classical conditioning, signal detection, and evolution.

Behavioural processes·2014
Same author

Timing in simple conditioning and occasion setting: a neural network approach.

Behavioural processes·2014
Same author

Effects of unexpected changes in visual scenes on the human acoustic startle response and prepulse inhibition.

Behavioural processes·2011
Same author

Spatial and temporal cognitive mapping: a neural network approach.

Trends in cognitive sciences·2011
Same author

Attenuation of auditory startle and prepulse inhibition by unexpected changes in ambient illumination through dopaminergic mechanisms.

Behavioural brain research·2008
Same author

Startle and prepulse inhibition as a function of background noise: a computational and experimental analysis.

Behavioural brain research·2006
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

We developed a neural network model that explains classical conditioning and brain function. This model integrates stimulus representation, attention, and prediction error signals within key brain loops, including the hippocampus.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Classical conditioning is a fundamental learning process.
  • Understanding the neural basis of learning and memory is a key challenge in neuroscience.
  • Existing models often lack the capacity to integrate diverse neurophysiological data.

Purpose of the Study:

  • To present a novel neural network model capable of characterizing classical conditioning paradigms.
  • To describe the effects of neurophysiological manipulations on learning and behavior.
  • To map the network's information processing loops onto brain structures.

Main Methods:

  • Development of a neural network with simple and configural stimulus representations.
  • Inclusion of attentional control mechanisms for storage and retrieval.

Related Experiment Videos

  • Mapping network components to specific brain information processing loops (hippocampal-cortical, hippocampal-cerebellar, etc.).

    • Main Results:

    • The network accurately describes real-time behavior in classical conditioning.
    • Simulations align with extensive data on hippocampal and septal neural activity.
    • Model predictions are consistent with effects of pharmacological and lesion manipulations.

    Conclusions:

    • The hippocampal formation computes aggregate environmental predictions and error signals for cortical learning.
    • The proposed neural network provides a unified framework for understanding classical conditioning and brain function.
    • The model offers insights into the roles of specific brain loops in learning and attention.