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Related Concept Videos

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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.
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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.
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Related Experiment Video

Updated: Mar 27, 2026

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Memory-induced mechanism for self-sustaining activity in networks.

A E Allahverdyan1, G Ver Steeg2, A Galstyan2

  • 1Yerevan Physics Institute, Alikhanian Brothers Street 2, Yerevan 375036, Armenia.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
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This study explores how collective activity sustains in networks using temporal memory, not network loops. Findings show nodes form synchronized clusters, even with changing connections, suggesting new models for social media dynamics.

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Area of Science:

  • Complex systems
  • Network science
  • Computational neuroscience

Background:

  • Existing models of self-sustaining activity often rely on network loops.
  • Social media networks typically lack long loops, necessitating alternative models.
  • Neuronal dynamics provide inspiration for understanding collective behavior.

Purpose of the Study:

  • To investigate a novel mechanism for sustained collective activity in networks.
  • To explore the role of temporal memory in sustaining activity, particularly in treelike structures.
  • To model phenomena relevant to social media dynamics.

Main Methods:

  • Studied a network model inspired by neuronal dynamics.
  • Focused on treelike network structures.
  • Incorporated temporal memory as a key activation mechanism.
  • Investigated the impact of weak behavioral noise and randomly weighted connections.

Main Results:

  • Nodes robustly split into several clusters under weak behavioral noise.
  • Partial synchronization of nodes within clusters was observed.
  • Randomly weighted connections, modeling attention redistribution, facilitate self-sustained activity.

Conclusions:

  • Temporal memory in nodes can drive self-sustaining collective activity in networks lacking long loops.
  • The proposed model offers a viable mechanism for understanding activity patterns in social media.
  • Network adaptability, through changing connection strengths, enhances collective activity persistence.