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

457
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...
457
Long-Term Memory01:18

Long-Term Memory

278
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...
278
Associative Learning01:27

Associative Learning

617
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
617
Cognitive Learning01:21

Cognitive Learning

680
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
680
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

981
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...
981
Understanding Memory01:19

Understanding Memory

659
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...
659

You might also read

Related Articles

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

Sort by
Same author

Sensory-memory interactions via modular structure explain errors in visual working memory.

eLife·2024
Same author

Mechanisms underlying sharpening of visual response dynamics with familiarity.

eLife·2019
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: Sep 24, 2025

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

681

Unsupervised learning for robust working memory.

Jintao Gu1, Sukbin Lim1,2

  • 1Neural Science, New York University Shanghai, Shanghai, China.

Plos Computational Biology
|May 2, 2022
PubMed
Summary
This summary is machine-generated.

Synaptic plasticity rules help stabilize neural networks for working memory. Combining differential and homeostatic plasticity robustly restores persistent activity in working memory models, mitigating connectivity tuning issues.

More Related Videos

Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children – Working Memory (CABC-WM)
09:05

Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children – Working Memory (CABC-WM)

Published on: June 12, 2017

30.0K
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

4.9K

Related Experiment Videos

Last Updated: Sep 24, 2025

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

681
Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children – Working Memory (CABC-WM)
09:05

Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children – Working Memory (CABC-WM)

Published on: June 12, 2017

30.0K
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

4.9K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Persistent neural activity is a key mechanism for working memory.
  • Attractor dynamics in neural networks can model this persistent activity.
  • Fine-tuning network connectivity is crucial for stable attractor dynamics, especially for continuous signals.

Purpose of the Study:

  • To investigate if synaptic plasticity rules can overcome connectivity tuning challenges in working memory models.
  • To evaluate the efficacy of differential and homeostatic plasticity in maintaining persistent activity.
  • To compare the performance of these plasticity rules in rate-coded and location-coded working memory models.

Main Methods:

  • Simulated two working memory models: rate-coded and location-coded persistent activity.
  • Applied differential plasticity to correct rapid activity changes.
  • Applied homeostatic plasticity to regularize long-term average activity.
  • Introduced perturbations in network connectivity to test model robustness.

Main Results:

  • Differential plasticity alone restored graded persistent activity after connectivity perturbations.
  • Differential plasticity partially recovered location-coded persistent activity, but patterns could be irregular.
  • Homeostatic plasticity robustly recovered smooth spatial patterns for specific synaptic perturbations.
  • Homeostatic plasticity was ineffective against outgoing synaptic perturbations from local populations.
  • Combining differential and homeostatic plasticity restored location-coded persistent activity across a wider range of perturbations.

Conclusions:

  • Synaptic plasticity rules can mitigate the need for precise connectivity tuning in working memory models.
  • Differential plasticity is effective for rate-coded memory and partially for location-coded memory.
  • Homeostatic plasticity offers robustness for spatial patterns but has limitations.
  • Combining differential and homeostatic plasticity provides a synergistic solution for robust location-coded working memory.