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

483
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...
483
Storage01:23

Storage

163
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...
163
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

1.3K
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
1.3K
Long-term Potentiation01:35

Long-term Potentiation

56.5K
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.
56.5K
Long-Term Memory01:18

Long-Term Memory

332
Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...
332
Understanding Memory01:19

Understanding Memory

704
Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
704

You might also read

Related Articles

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

Sort by
Same author

Task-induced topological and geometrical changes in whole-brain dynamics predict cognitive individual differences.

bioRxiv : the preprint server for biology·2026
Same author

Geometry of neural dynamics along the cortical attractor landscape reflects changes in attention.

Nature communications·2026
Same author

Two views of the brain are reconciled by a unifying principle of maximal information processing.

bioRxiv : the preprint server for biology·2025
Same author

Identification of modulated whole-brain dynamical models from nonstationary electrophysiological data.

Journal of neural engineering·2025
Same author

Short-term sensory memory mediates adaptation, habituation, and a paradoxical neural-behavioral transformation in <i>C. elegans</i>.

bioRxiv : the preprint server for biology·2025
Same author

Dynamical models reveal anatomically reliable attractor landscapes embedded in resting state brain networks.

bioRxiv : the preprint server for biology·2024
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
Same journal

CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.

PLoS computational biology·2026
Same journal

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli.

PLoS computational biology·2026
Same journal

Neuronal excitability and parameter variability in the Hodgkin-Huxley model.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Oct 20, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.4K

Slow manifolds within network dynamics encode working memory efficiently and robustly.

Elham Ghazizadeh1, ShiNung Ching1,2,3

  • 1Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America.

Plos Computational Biology
|September 15, 2021
PubMed
Summary
This summary is machine-generated.

Researchers modeled neural networks to understand working memory. They discovered that memories encoded on slow manifolds are efficient and noise-robust, offering new insights into neural circuit dynamics.

More Related Videos

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.3K
An Appetitive Spatial Working Memory Task for Mice in a Semi-Automated 8-Arm Radial Maze, Reducing Fearful Memory Association in the Maze
14:24

An Appetitive Spatial Working Memory Task for Mice in a Semi-Automated 8-Arm Radial Maze, Reducing Fearful Memory Association in the Maze

Published on: July 29, 2025

908

Related Experiment Videos

Last Updated: Oct 20, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.4K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.3K
An Appetitive Spatial Working Memory Task for Mice in a Semi-Automated 8-Arm Radial Maze, Reducing Fearful Memory Association in the Maze
14:24

An Appetitive Spatial Working Memory Task for Mice in a Semi-Automated 8-Arm Radial Maze, Reducing Fearful Memory Association in the Maze

Published on: July 29, 2025

908

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Working memory is essential for temporary information storage and manipulation, critical for cognitive tasks.
  • Understanding the network-level mechanisms underlying working memory remains a significant challenge in neuroscience.

Purpose of the Study:

  • To investigate network-level mechanisms of working memory using a computational modeling approach.
  • To explore how recurrent neural networks implement working memory functions.

Main Methods:

  • Optimization of thousands of recurrent rate-based neural networks on a working memory task.
  • Application of dynamical systems analysis to the optimized neural networks.

Main Results:

  • Identification of four distinct dynamical mechanisms for working memory.
  • Prevalence of a mechanism where memories are encoded on slow stable manifolds, characterized by phasic neuronal activation.
  • These networks exhibit natural forgetting but are more efficient and robust to noise compared to attractor-based mechanisms.

Conclusions:

  • Working memory can be implemented through dynamics involving slow stable manifolds, leading to phasic activation.
  • This mechanism offers a trade-off between forgetting and efficiency/noise robustness.
  • Provides novel hypotheses for how neural circuit dynamics encode working memory.