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

System of Memory01:23

System of Memory

Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
Long-Term Memory01:18

Long-Term Memory

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...
Storage01:23

Storage

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

Higher Mental Functions of Brain: Learning and Memory

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 playing an...
Sensory Memory01:14

Sensory Memory

Sensory memory captures information from the environment in its original form for a very brief duration, just long enough to be exposed to visual, auditory, and other senses. This type of memory is detailed and rich but quickly lost unless certain strategies are employed to transfer it into short-term or long-term memory. Sensory information is continuously bombarding the human brain, yet only a small fraction is absorbed, as most of it does not significantly impact daily life. For instance,...

You might also read

Related Articles

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

Sort by
Same author

Colorimetric sensing array based on Fe single-atom nanozyme for ultrasensitively discriminating organophosphorus pesticides.

Analytica chimica acta·2026
Same author

WormSORT: A detection-based multiple object tracking model for individual silkworms in breeding environments.

PLoS computational biology·2026
Same author

Research progress on chemical metabolites, processing technologies, and pharmacological activities of asperosaponin VI: a systematic review and critical evaluation.

Frontiers in pharmacology·2026
Same author

Artificial intelligence-assisted detection of epileptic spasms using electroencephalographic-video analysis.

Epilepsia·2026
Same author

Epidemiological characteristics and incidence prediction analysis of brucellosis in Bayingolin mongol autonomous prefecture, Xinjiang.

BMC infectious diseases·2026
Same author

Patient satisfaction and its influencing factors: results from a survey in inpatient department in a tertiary hospital setting in China.

BMC health services research·2026
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

Related Experiment Video

Updated: Jun 24, 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

Spatio-temporal memories for machine learning: a long-term memory organization.

Janusz A Starzyk1, Haibo He

  • 1School of Electrical Engineering and Computer Science, Ohio University, Athens, OH 45701, USA. starzyk@bobcat.ent.ohiou.edu

IEEE Transactions on Neural Networks
|April 2, 2009
PubMed
Summary
This summary is machine-generated.

We developed a novel, biologically inspired artificial neural structure for reliable long-term memory (LTM) crucial for machine intelligence. This adaptable and robust architecture mimics natural intelligence, enhancing learning and goal-driven behavior.

Related Experiment Videos

Last Updated: Jun 24, 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

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Computer Science

Background:

  • Reliable and flexible long-term memory (LTM) is essential for advancing machine intelligence.
  • Current artificial neural structures often lack the sophisticated memory capabilities of natural intelligence.
  • Mimicking biological memory mechanisms is a key challenge in developing advanced AI.

Purpose of the Study:

  • To propose a novel, biologically inspired long-term memory (LTM) architecture.
  • To design a foundational building block for neuron-level architectures capable of learning, anticipation, and goal-driven behavior.
  • To detail the mechanisms of hierarchical memory organization, storage, recognition, and recall.

Main Methods:

  • Development of a novel, biologically inspired LTM architecture.
  • Implementation of a mutual input enhancement and blocking structure.
  • Hierarchical organization of memory with detailed storage, recognition, and recall mechanisms.
  • Simulation and performance evaluation of the proposed memory architecture.

Main Results:

  • The proposed LTM architecture demonstrates effectiveness, adaptability, and robustness in simulations.
  • The architecture facilitates learning, anticipation, and goal-driven behavior in artificial neural systems.
  • Accuracy was validated through comparisons with established methods like Levenshtein distance and Markov chains.

Conclusions:

  • The novel LTM architecture provides a promising foundation for creating more capable artificial intelligence.
  • Biologically inspired designs are effective for developing advanced memory functions in machines.
  • The proposed structure offers a robust and adaptable solution for spatio-temporal memory in AI.