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

151
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...
151
Hindsight Biases01:12

Hindsight Biases

4.0K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
4.0K
Chunking and Rehearsal in Sensory Memory01:22

Chunking and Rehearsal in Sensory Memory

320
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...
320
Long-Term Memory01:18

Long-Term Memory

294
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...
294
System of Memory01:23

System of Memory

6.6K
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...
6.6K
Autobiographical Memory01:14

Autobiographical Memory

6.0K
Autobiographical memory is a unique type of episodic memory that involves recollecting personal life experiences. It allows individuals to remember significant events from their past, creating a narrative of their lives. One interesting phenomenon related to autobiographical memory is the reminiscence bump. This effect refers to the tendency of adults to recall more events from their second and third decades of life — typically between ages 10 to 30 — than from other periods. This...
6.0K

You might also read

Related Articles

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

Sort by
Same author

Continuous Attractor Networks for Laplace Neural Manifolds.

Computational brain & behavior·2026
Same author

Learning Temporal Relationships Between Symbols with Laplace Neural Manifolds.

Computational brain & behavior·2026
Same author

Hippocampal astrocytic sequences emerge during learning and memory.

bioRxiv : the preprint server for biology·2026
Same author

Hierarchical temporal receptive windows and zero-shot timescale generalization in biologically constrained scale-invariant deep networks.

ArXiv·2026
Same author

Ramping dynamics in the frontal cortex unfold over multiple timescales during motor planning.

Journal of neurophysiology·2025
Same author

Ramping cells in the rodent medial prefrontal cortex encode time to past and future events via real Laplace transform.

Proceedings of the National Academy of Sciences of the United States of America·2024

Related Experiment Video

Updated: Oct 6, 2025

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
06:35

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

Published on: April 28, 2016

34.3K

Predicting the Future With a Scale-Invariant Temporal Memory for the Past.

Wei Zhong Goh1, Varun Ursekar2, Marc W Howard3

  • 1Graduate Program in Neuroscience, Boston University, Boston, MA 02215, U.S.A. weizhong@bu.edu.

Neural Computation
|January 13, 2022
PubMed
Summary

This study introduces a novel algorithm for predicting future events using a scale-invariant temporal memory of the past. The neurally inspired model effectively forecasts future occurrences based on past data, demonstrating robust performance on complex temporal processes.

More Related Videos

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

10.9K

Related Experiment Videos

Last Updated: Oct 6, 2025

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
06:35

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

Published on: April 28, 2016

34.3K
The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

10.9K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • The brain possesses a remarkable capacity for temporal memory, retaining information about past events over extended durations.
  • Understanding the neural mechanisms underlying temporal memory is crucial for developing predictive models of cognition.
  • Existing models often struggle with scale invariance and the complexity of real-world temporal data.

Purpose of the Study:

  • To develop a neurally inspired algorithm for scale-invariant prediction of future events.
  • To utilize a temporal representation of past events to forecast future occurrences.
  • To create a time-local algorithm that assigns credit based on predictive impact.

Main Methods:

  • A novel algorithm was developed, inspired by neural mechanisms of temporal memory.
  • The algorithm employs a scale-invariant temporal representation of past events.
  • Model performance was evaluated on simultaneous renewal processes with varying timescales.

Main Results:

  • The algorithm successfully generates scale-invariant predictions of future events.
  • It provides a scale-invariant estimate of future events as a function of their expected occurrence time.
  • The model demonstrated effective scaling on complex renewal processes, even those requiring exponentially many states for Markov modeling.

Conclusions:

  • The proposed algorithm offers a powerful new approach to temporal prediction, leveraging scale-invariant representations.
  • This neurally inspired method shows promise for applications in neuroscience and artificial intelligence.
  • The algorithm's efficiency in handling complex temporal dynamics highlights its potential for modeling real-world phenomena.