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

Working Memory01:24

Working Memory

Working memory refers to a combination of components, including short-term memory and attention, that allow an individual to hold information temporarily as we perform cognitive tasks. It is an essential cognitive function that enables the execution of complex tasks such as problem-solving, comprehension, and reasoning. Unlike short-term memory, which simply involves the storage of information for a brief period, working memory involves the active manipulation and processing of this information.
Long-term Potentiation01:35

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Long-term Potentiation01:25

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when presynaptic neurons...
Chunking and Rehearsal in Sensory Memory01:22

Chunking and Rehearsal in Sensory Memory

Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of information more...

You might also read

Related Articles

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

Sort by
Same author

Synchrony timescales underlie irregular neocortical spiking.

Neuron·2025
Same author

Reward-driven adaptation of movements requires strong recurrent basal ganglia-cortical loops.

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

The fluctuation-based regime of thalamocortical circuitry.

bioRxiv : the preprint server for biology·2025
Same author

Interactions between long- and short-term synaptic plasticity transform temporal neural representations into spatial.

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

Corrigendum to "Mathematical models of learning and what can be learned from them" [Curr Opin Neurobiol 80 (2023)].

Current opinion in neurobiology·2025
Same author

Random Tree Model of Meaningful Memory.

Physical review letters·2025
Same journal

AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.

Frontiers in computational neuroscience·2026
Same journal

Modeling multiscale neural dynamics for EEG-based emotion recognition using an attentive wavelet-transformer framework.

Frontiers in computational neuroscience·2026
Same journal

New directions for complex systems in contemporary neuroscience: a morphodynamic and emergent function approach.

Frontiers in computational neuroscience·2026
Same journal

NMDA receptor kinetics drive distinct routes to chaotic firing in pyramidal neurons.

Frontiers in computational neuroscience·2026
Same journal

Schumann-anchored golden ratio organization of human neural oscillations.

Frontiers in computational neuroscience·2026
Same journal

Toward model-guided electrophysiology-Encoding of chirps in the electrosensory periphery of <i>Apteronotus leptorhynchus</i>.

Frontiers in computational neuroscience·2026
See all related articles

Related Experiment Video

Updated: May 28, 2026

Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment
07:01

Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment

Published on: September 20, 2020

Short-Term Facilitation may Stabilize Parametric Working Memory Trace.

Vladimir Itskov1, David Hansel, Misha Tsodyks

  • 1Department of Mathematics, University of Nebraska-Lincoln Lincoln, NE, USA.

Frontiers in Computational Neuroscience
|October 27, 2011
PubMed
Summary
This summary is machine-generated.

Networks modeling parametric working memory (WM) are unstable. Short-term synaptic facilitation stabilizes these networks by slowing activity drift, making them suitable for WM models.

Keywords:
Ring modelbump attractorcontinuous attractorsinhomogeneous neural medianeural fieldsparametric working memorysynaptic facilitationworking memory

More Related Videos

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions
10:38

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions

Published on: July 16, 2015

Improved Preparation and Preservation of Hippocampal Mouse Slices for a Very Stable and Reproducible Recording of Long-term Potentiation
09:39

Improved Preparation and Preservation of Hippocampal Mouse Slices for a Very Stable and Reproducible Recording of Long-term Potentiation

Published on: June 26, 2013

Related Experiment Videos

Last Updated: May 28, 2026

Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment
07:01

Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment

Published on: September 20, 2020

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions
10:38

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions

Published on: July 16, 2015

Improved Preparation and Preservation of Hippocampal Mouse Slices for a Very Stable and Reproducible Recording of Long-term Potentiation
09:39

Improved Preparation and Preservation of Hippocampal Mouse Slices for a Very Stable and Reproducible Recording of Long-term Potentiation

Published on: June 26, 2013

Area of Science:

  • Computational neuroscience
  • Cognitive neuroscience

Background:

  • Networks with continuous attractors are paradigmatic models for parametric working memory (WM).
  • These models require fine-tuning of connections, rendering them structurally unstable.
  • Existing models exhibit activity drift without stabilizing stimuli.

Purpose of the Study:

  • To analyze the stability and functionality of ring attractor networks for working memory.
  • To investigate the impact of imperfectly tuned connections on network dynamics.
  • To explore the potential of short-term synaptic facilitation in enhancing working memory models.

Main Methods:

  • Derivation of an analytical expression for drift dynamics in ring attractor networks.
  • Analysis of network behavior with imperfectly tuned connections.
  • Extension of drift velocity calculations to networks incorporating short-term synaptic facilitation.

Main Results:

  • Ring attractor networks without fine-tuning exhibit significant activity drift.
  • The network cannot sustain working memory for several seconds due to drift.
  • Short-term synaptic facilitation substantially slows down the activity bump's drift velocity.

Conclusions:

  • Standard ring attractor networks are not robust models for working memory over extended periods.
  • Short-term synaptic facilitation significantly enhances the robustness of attractor networks.
  • Facilitation-enhanced networks are suitable models for parametric working memory.