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

Long-term Depression01:03

Long-term Depression

2.5K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Calcium Ion Concentration Mechanism
If over...
2.5K
Long-term Potentiation01:35

Long-term Potentiation

54.8K
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.
54.8K
Interference and Decay01:16

Interference and Decay

107
Forgetting is a complex cognitive phenomenon influenced by several factors, among which interference and decay are particularly prominent. These processes explain why individuals often struggle to retrieve specific information from memory, leading to lapses in recall that can be observed in everyday situations.
Interference occurs when competing memories hinder the retrieval of particular information. It can be classified into two types: proactive and retroactive interference. Proactive...
107
Long-Term Memory01:18

Long-Term Memory

107
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...
107
Role of Neurotransmitters in Memory01:23

Role of Neurotransmitters in Memory

420
Neurotransmitters are integral to the brain's communication system, enabling neurons to transmit signals across synapses. This chemical exchange underpins various cognitive functions, including memory processes. The role of neurotransmitters in memory is multifaceted, influencing the encoding, consolidation, and retrieval of memories through their action on different neural circuits.
 Glutamate and Synaptic Plasticity
Glutamate, the brain's main excitatory neurotransmitter, is...
420
Forgetting01:21

Forgetting

53
Forgetting is an intrinsic aspect of human memory, characterized by the gradual loss or inaccessibility of information over time. Hermann Ebbinghaus, a pioneering psychologist, extensively studied this phenomenon and formulated the forgetting curve. This curve illustrates that memory loss occurs rapidly immediately after learning and then decelerates over time. Several mechanisms contribute to forgetting, including encoding failure, storage decay, retrieval failure, and interference.
Encoding...
53

You might also read

Related Articles

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

Sort by
Same author

Evanescent random walker on networks: Hitting times, budget renewal, and survival dynamics.

Chaos (Woodbury, N.Y.)·2025
Same author

Group identity without social interactions?

The Behavioral and brain sciences·2025
Same author

Random walks with stochastic resetting in complex networks: A discrete-time approach.

Chaos (Woodbury, N.Y.)·2025
Same author

Channel-facilitated transport under resetting dynamics.

The Journal of chemical physics·2024
Same author

Stochastic Compartment Model with Mortality and Its Application to Epidemic Spreading in Complex Networks.

Entropy (Basel, Switzerland)·2024
Same author

Lévy movements and a slowly decaying memory allow efficient collective learning in groups of interacting foragers.

PLoS computational biology·2023
Same journal

Gap junction architecture and synchronization clusters in the thalamic reticular nuclei.

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

Exact computation of Lyapunov exponents via system parameters in multi-triangle chaotic maps: Bifurcation analysis and circuit realization.

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

Integrating score-based generative modeling and neural ODEs for accurate representation of multiscale chaotic dynamics.

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

A data-driven tuberculosis model with behavioral changes and saturated treatment: Optimal control and cost-effectiveness study.

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

Breathers, rational solutions, and their exact physical spectra in F = 1 spinor Bose-Einstein condensates.

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

Finite invariant sets with bridging points in logistic IFS.

Chaos (Woodbury, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Jun 3, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

483

Random walks with long-range memory on networks.

Ana Gabriela Guerrero-Estrada1, Alejandro P Riascos2, Denis Boyer1

  • 1Instituto de Física, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.

Chaos (Woodbury, N.Y.)
|January 9, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a solvable random walk model with memory on networks. It shows that walker revisits nodes preferentially, leading to power-law decay instead of exponential decay in occupation probability.

More Related Videos

The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents
09:01

The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents

Published on: July 8, 2015

12.5K
Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

41.7K

Related Experiment Videos

Last Updated: Jun 3, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

483
The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents
09:01

The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents

Published on: July 8, 2015

12.5K
Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

41.7K

Area of Science:

  • Complex Systems
  • Statistical Physics
  • Network Science

Background:

  • Random walks are fundamental models for transport phenomena.
  • Standard random walks on networks exhibit exponential decay in occupation probabilities.
  • Memory effects can alter the dynamics of random walks.

Purpose of the Study:

  • To introduce and analyze an exactly solvable random walk model with long-range memory on arbitrary networks.
  • To investigate the impact of preferential node revisitation on walker dynamics.
  • To explore the relaxation dynamics and stationary states of such memory-endowed random walks.

Main Methods:

  • Developing an exactly solvable model for random walks with preferential memory.
  • Expressing occupation probability as a sum over eigenmodes of the network's random walk matrix.
  • Analyzing the late-time relaxation dynamics and critical self-organization.

Main Results:

  • Occupation probability amplitudes decay as power-laws at large times, deviating from exponential decay.
  • The stationary state remains unchanged compared to memory-less random walks, fulfilling detailed balance.
  • Late-time relaxation is dominated by a single power-law, critically self-organized and dependent on network eigenvalues and resetting probability.

Conclusions:

  • The introduced random walk model with preferential memory offers a new framework for studying transport phenomena.
  • Power-law decay in occupation probability signifies a departure from standard random walk behavior due to memory.
  • The findings are applicable to various real-world complex networks and phenomena like human mobility and epidemic spreading.