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

Long-term Depression01:03

Long-term Depression

3.0K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Calcium Ion Concentration Mechanism
If over...
3.0K
Long-term Depression01:05

Long-term Depression

33.0K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
33.0K
Long-term Potentiation01:25

Long-term Potentiation

3.3K
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...
3.3K
Long-term Potentiation01:35

Long-term Potentiation

58.1K
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.
58.1K
Role of Neurotransmitters in Memory01:23

Role of Neurotransmitters in Memory

2.4K
Neurotransmitters are integral to the brain's communication system, enabling neurons to transmit signals across synapses. This chemical exchange underpins various cognitive functions, including memory processes. The role of neurotransmitters in memory is multifaceted, influencing the encoding, consolidation, and retrieval of memories through their action on different neural circuits.
 Glutamate and Synaptic Plasticity
Glutamate, the brain's main excitatory neurotransmitter, is...
2.4K
Storage01:23

Storage

319
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...
319

You might also read

Related Articles

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

Sort by
Same author

Emergent Homeostasis and Degeneracy From Multi-Dimensional Attractors.

BioEssays : news and reviews in molecular, cellular and developmental biology·2026
Same author

Optimization and variability can coexist.

ArXiv·2025
Same author

Aligned and oblique dynamics in recurrent neural networks.

eLife·2024
Same author

NMDA receptors regulate the firing rate set point of hippocampal circuits without altering single-cell dynamics.

Neuron·2024
Same author

The Brain's Best Kept Secret Is Its Degenerate Structure.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2024
Same author

Representational drift as a result of implicit regularization.

eLife·2024

Related Experiment Video

Updated: Jan 6, 2026

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

27.6K

Stable memory with unstable synapses.

Lee Susman1,2, Naama Brenner3,4, Omri Barak5,6

  • 1Interdisciplinary Program in Applied Mathematics, Technion Israel Institute of Technology, Haifa, 32000, Israel. lee.susman@gmail.com.

Nature Communications
|October 2, 2019
PubMed
Summary
This summary is machine-generated.

Long-term memories may be stored in global network connectivity, not just individual synapses. This dynamic attractor model explains memory resilience despite synaptic fluctuations and homeostatic stabilization.

More Related Videos

Investigation of Synaptic Tagging/Capture and Cross-capture using Acute Hippocampal Slices from Rodents
11:29

Investigation of Synaptic Tagging/Capture and Cross-capture using Acute Hippocampal Slices from Rodents

Published on: September 4, 2015

14.6K
A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
08:05

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

Published on: January 5, 2018

10.1K

Related Experiment Videos

Last Updated: Jan 6, 2026

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

27.6K
Investigation of Synaptic Tagging/Capture and Cross-capture using Acute Hippocampal Slices from Rodents
11:29

Investigation of Synaptic Tagging/Capture and Cross-capture using Acute Hippocampal Slices from Rodents

Published on: September 4, 2015

14.6K
A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
08:05

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

Published on: January 5, 2018

10.1K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • The prevailing view attributes memory to stable synaptic efficacy, but evidence shows synaptic fluctuations.
  • How memories persist despite these dynamic changes and homeostatic mechanisms is unclear.

Purpose of the Study:

  • To explore memory storage in global network connectivity amidst individual connection fluctuations.
  • To investigate the resilience of memories stored as dynamic attractors versus fixed points.

Main Methods:

  • Modeling neural network dynamics.
  • Analyzing the impact of homeostatic stabilization on connectivity.
  • Simulating memory storage and retrieval using biologically plausible learning rules.

Main Results:

  • Memories stored as time-varying dynamic attractors are more resilient to synaptic fluctuations than fixed-point representations.
  • Homeostatic stabilization differentially affects network connectivity aspects.
  • Dynamic attractors can be learned via biologically plausible rules and support associative retrieval.

Conclusions:

  • Memory storage may involve global network properties rather than solely stable individual connections.
  • Dynamic attractor models offer a framework for understanding memory persistence.
  • The study links learning-rule properties to network-level memory representations and suggests experimental signatures.