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

Neural Circuits01:25

Neural Circuits

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...
Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

You might also read

Related Articles

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

Sort by
Same author

Reply to: On the interpretation of astrocytic calcium signalling with graphene oxide electrodes.

Nature nanotechnologyĀ·2026
Same author

A Mechanistic Understanding of the Different Factors Affecting Adaptation and Flexibility During Probabilistic Reversal Learning: A Neurocomputational Study on Patients with Parkinson's Disease and Control Subjects.

Annals of biomedical engineeringĀ·2026
Same author

On the virtues and limitations of Granger-causal brain connectivity estimate: Critical analysis using neural mass models.

Network neuroscience (Cambridge, Mass.)Ā·2026
Same author

Investigating Key Factors Influencing Behavioral Adaptation In Parkinsonian Subjects Through Neurocomputational Model Of The Basal Ganglia.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International ConferenceĀ·2025
Same author

Septohippocampal acetylcholine and theta oscillations can modulate memory encoding and retrieval: Insights from a neural masses network.

Brain research bulletinĀ·2025
Same author

Modeling the Role of the Alpha Rhythm in Attentional Processing during Distractor Suppression.

Journal of cognitive neuroscienceĀ·2025

Related Experiment Video

Updated: May 11, 2026

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

A multi-layer neural-mass model for learning sequences using theta/gamma oscillations.

Filippo Cona1, Mauro Ursino

  • 1Department of Electronics, Computer Sciences and Systems, University of Bologna, Via Venezia, 52, Cesena (FC), 47521, Italy. filippo.cona2@unibo.it

International Journal of Neural Systems
|May 1, 2013
PubMed
Summary

A new neural mass model learns sequences using three cortical layers and theta/gamma rhythms. This computational neuroscience model explains sequence recall and phase precession.

More Related Videos

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
07:33

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Published on: June 29, 2018

Generation of Local CA1 γ Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

Related Experiment Videos

Last Updated: May 11, 2026

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
07:33

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Published on: June 29, 2018

Generation of Local CA1 γ Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

Area of Science:

  • Computational neuroscience
  • Neural modeling
  • Cognitive neuroscience

Background:

  • Understanding sequence memorization is crucial for cognitive functions.
  • Existing models often lack detailed mechanisms for temporal learning.
  • Neural oscillations, like theta and gamma rhythms, are implicated in memory processes.

Purpose of the Study:

  • To present a novel neural mass model for sequence memorization.
  • To investigate the role of distinct cortical layers and oscillatory rhythms in learning.
  • To computationally account for phenomena like sequence recovery and phase precession.

Main Methods:

  • Developed a three-layer neural mass model simulating cortical columns.
  • Implemented auto-associative memory in the theta range and object segmentation in the gamma range.
  • Utilized Hebbian and anti-Hebbian learning rules for sequence acquisition and feedback interactions.

Main Results:

  • The model successfully learned and recalled sequences.
  • Demonstrated the capacity for segmenting objects within the gamma frequency band.
  • The network's dynamics accounted for the observed phase-precession phenomenon.

Conclusions:

  • The proposed neural mass model provides a viable mechanism for sequence memorization.
  • Theta and gamma rhythms play distinct but interacting roles in learning and recall.
  • The model offers insights into the neural basis of memory and learning.