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

Storage01:23

Storage

83
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...
83
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

739
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...
739
Neural Circuits01:25

Neural Circuits

1.1K
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...
1.1K
Cyclic Processes And Isolated Systems01:19

Cyclic Processes And Isolated Systems

2.7K
A thermodynamic system with zero heat exchange and work is an isolated system. For these systems, the internal energy remains constant.
In the case of a non-isolated system, the change in the internal energy is zero only if the process is cyclic. A thermodynamic process is considered cyclic if the system undergoes a series of changes and returns to its initial state. 
Consider a cyclic process that returns to its initial state, undergoing a four-step process. The heat transfer along each...
2.7K
Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

545
Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
545
Convolution Properties I01:20

Convolution Properties I

147
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
147

You might also read

Related Articles

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

Sort by
Same author

Emotional words evoke region- and valence-specific patterns of concurrent neuromodulator release in human thalamus and cortex.

Cell reports·2026
Same author

Existing and preferred organizational culture in Algerian power plants: a second look.

Frontiers in sociology·2026
Same author

Dopamine dynamics in human anterior cingulate cortex during Pavlovian-instrumental conflict.

bioRxiv : the preprint server for biology·2026
Same author

Optimizing disorder with machine learning to harness phase synchronization.

Chaos (Woodbury, N.Y.)·2026
Same author

Controlling severe atopic dermatitis dynamics.

Chaos (Woodbury, N.Y.)·2026
Same author

A combined experimental/individual differences examination of the influence of motivation on cognitive ability assessments.

Memory & cognition·2026
Same journal

Demonstration of a quantum C-NOT gate in a time-multiplexed fully reconfigurable photonic processor.

Nature communications·2026
Same journal

Nonlinear quantum light source with van der Waals ferroelectric NbOX<sub>2</sub> (X = Br, I).

Nature communications·2026
Same journal

Antagonistic histone H2A variants and autonomous heterochromatin formation shape epigenomic patterns in Arabidopsis.

Nature communications·2026
Same journal

The long tail of nitrate pollution in groundwater challenges governance of global water quality.

Nature communications·2026
Same journal

Select microbial metabolites promote tau aggregation in a murine tauopathy model.

Nature communications·2026
Same journal

Warming climate has lengthened global intense tropical cyclone seasons.

Nature communications·2026
See all related articles

Related Experiment Video

Updated: Jun 24, 2025

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
11:32

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

3.4K

Reservoir-computing based associative memory and itinerancy for complex dynamical attractors.

Ling-Wei Kong1,2, Gene A Brewer3, Ying-Cheng Lai4,5

  • 1Department of Computational Biology, Cornell University, Ithaca, New York, USA.

Nature Communications
|June 6, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces reservoir computing for complex dynamical memories, enabling storage and retrieval of multiple attractors. It details methods for both location-addressable and content-addressable recall, advancing memory system research.

More Related Videos

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures
16:01

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Published on: August 1, 2011

26.4K
An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
08:59

An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

Published on: March 3, 2023

2.1K

Related Experiment Videos

Last Updated: Jun 24, 2025

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
11:32

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

3.4K
Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures
16:01

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Published on: August 1, 2011

26.4K
An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
08:59

An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

Published on: March 3, 2023

2.1K

Area of Science:

  • Computational neuroscience
  • Complex systems
  • Machine learning

Background:

  • Traditional neural networks store static patterns.
  • Associative memories are crucial for information processing.
  • Dynamical attractors represent complex temporal patterns.

Purpose of the Study:

  • To develop reservoir-computing based memories for complex dynamical attractors.
  • To investigate location-addressable and content-addressable retrieval mechanisms.
  • To provide foundational insights for long-term memory and itinerancy.

Main Methods:

  • Utilizing reservoir computing machines for memory.
  • Implementing location-addressable retrieval with an index channel.
  • Employing multistability and cue signals for content-addressable retrieval.

Main Results:

  • A single reservoir computing machine can memorize numerous periodic and chaotic attractors.
  • Control strategies for successful switching among attractors were articulated.
  • High success rates for content-addressable retrieval were achieved with sufficient cue signal length.

Conclusions:

  • Reservoir computing offers a framework for complex dynamical memories.
  • The study elucidates mechanisms for attractor switching and retrieval.
  • Findings contribute to developing advanced memory systems for dynamical patterns.