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Self-organized criticality in developing neuronal networks.

Christian Tetzlaff1, Samora Okujeni, Ulrich Egert

  • 1Bernstein Center for Computational Neuroscience, Institute of Physics III - Biophysics, Georg-August Universität, Göttingen, Germany. tetzlaff@physik3.gwdg.de

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|December 15, 2010
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Summary
This summary is machine-generated.

Developing neural networks naturally progress through distinct phases to achieve self-organized criticality, a stable state crucial for optimal information processing. This critical state ensures balanced network activity, preventing both signal extinction and pathological patterns.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Neural networks can exhibit self-organized criticality, a state beneficial for information flow.
  • Deviations from criticality lead to detrimental network states like signal extinction (subcritical) or epileptiform activity (supercritical).
  • The developmental trajectory of neural networks towards criticality remains poorly understood.

Purpose of the Study:

  • To investigate the developmental phases of cortical cell cultures leading to stable criticality.
  • To model the emergent connectivity dynamics during network maturation.
  • To elucidate the interplay between neuronal activity and synaptic plasticity in achieving a critical state.

Main Methods:

  • Monitoring cortical cell cultures (n=20) from 13 to 95 days in vitro (DIV).
  • Employing network modeling and mathematical analysis to study emergent connectivity.
  • Simulating synaptic development based on neuronal firing rate homeostasis.

Main Results:

  • Identified four distinct developmental phases: low-activity, supercritical, subcritical, and finally stable criticality (reached around 58 DIV).
  • Predicted a specific maturation pattern for inhibition, including early strong onset and delayed pruning.
  • Demonstrated a strong link between total synaptic connectivity and the balance of excitation and inhibition.

Conclusions:

  • The transition to criticality is guided by the dynamic interplay between neural activity and synaptic connectivity.
  • This developmental process suggests criticality is a generic and stable state for in vivo and in vitro neural networks.
  • Neuronal drive for firing rate homeostasis shapes synaptic development and network maturation towards criticality.