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Related Experiment Video

Updated: Dec 7, 2025

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
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Circadian rhythms in temporal-network connectivity.

T Alakörkkö1, J Saramäki1

  • 1Department of Computer Science, Aalto University, 02150 Espoo, Finland.

Chaos (Woodbury, N.Y.)
|October 2, 2020
PubMed
Summary
This summary is machine-generated.

Human communication networks exhibit daily rhythms in their structure. At night, more communication contacts are linked in sequences, suggesting information transfer, while daytime contacts are more independent.

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Area of Science:

  • Complex systems
  • Network science
  • Human dynamics

Background:

  • Human activities, including online interactions, demonstrate clear circadian rhythms.
  • Previous research has identified daily patterns in individual and population-level online activity.
  • Circadian patterns in communication network structure, beyond simple activity frequency, remain largely unexplored.

Purpose of the Study:

  • To investigate the temporal dynamics of communication network structure throughout the day.
  • To analyze how sequences of communication events, indicative of information transfer, vary over a 24-hour cycle.
  • To uncover circadian variations in network connectivity patterns.

Main Methods:

  • Analysis of temporal communication network data.
  • Focus on sequences of communication events within short timeframes.
  • Quantification of temporal connectivity patterns across different times of day.

Main Results:

  • Temporal connectivity in communication networks exhibits a circadian rhythm.
  • A higher proportion of contacts are part of communication sequences during nighttime hours.
  • Communication contacts appear more independent during daytime.

Conclusions:

  • Network structure, not just activity levels, follows a circadian pattern.
  • Nighttime network connectivity is characterized by more sequential communication, potentially related to information flow.
  • Daytime network connectivity shows more independent interactions, revealing richer temporal variations in networks.