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 Experiment Videos

Study of synaptic plasticity via random graphs.

Tatyana S Turova1

  • 1Mathematical Center, Lund University, S-22100 Lund, Sweden. tatyana@maths.lth.se

Bio Systems
|December 3, 2002
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Visual cortical networks for "What" and "Where" to the human hippocampus revealed with dynamical graphs.

Cerebral cortex (New York, N.Y. : 1991)·2025
Same author

Structural phase transitions in neural networks.

Mathematical biosciences and engineering : MBE·2013
Same author

The emergence of connectivity in neuronal networks: from bootstrap percolation to auto-associative memory.

Brain research·2011
Same author

On a phase diagram for random neural networks with embedded spike timing dependent plasticity.

Bio Systems·2007
Same author

Dynamical random graphs with memory.

Physical review. E, Statistical, nonlinear, and soft matter physics·2002

This study introduces dynamical random graphs for biological neural networks. Cycles emerge as stable structures, crucial for neuronal synchronization when using the Hebb rule.

Area of Science:

  • Computational Neuroscience
  • Network Science
  • Systems Biology

Background:

  • Biological neural networks exhibit complex dynamics.
  • Understanding network stability and information processing is crucial.
  • Dynamical random graphs offer a framework to model these networks.

Purpose of the Study:

  • To introduce and study dynamical random graphs for biological neural networks.
  • To identify parameters enabling stable, strongly connected neuronal groups.
  • To investigate the role of network structures in neuronal synchronization.

Main Methods:

  • Introduction and analysis of dynamical random graphs.
  • Phase diagram analysis of single neuron and synaptic strength parameters.
  • Implementation of the Hebb rule in excitatory neuronal network dynamics.

Related Experiment Videos

Main Results:

  • Identification of parameters for stable, strongly connected neuronal groups.
  • Demonstration that cycles are the most stable structures under Hebbian dynamics.
  • Revealed the role of cycles in synchronizing neuronal activity.

Conclusions:

  • Dynamical random graphs provide insights into neural network organization.
  • Cyclical structures are key to stability and synchronization in Hebbian networks.
  • This framework aids in understanding large-scale neuronal coordination.