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Short-term synaptic plasticity and network behavior.

W M Kistler1, J L van Hemmen

  • 1Theoretische Physik, Technischen Universitat München, James-Franck-Strasse, Garching bei M7uuml;nchen D-85747, Germany.

Neural Computation
|September 22, 1999
PubMed
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We present a new model for short-term synaptic plasticity, capturing both depression and facilitation. This model reveals complex network dynamics and stable firing patterns in interconnected neurons.

Area of Science:

  • Computational Neuroscience
  • Neuroscience
  • Complex Systems

Background:

  • Short-term synaptic plasticity (STP) influences neural network dynamics.
  • Existing models often focus on either facilitation or depression, not both.

Purpose of the Study:

  • To develop a unified, time-continuous model for use-dependent synaptic short-term plasticity.
  • To analyze network behavior in response to stimuli under STP.

Main Methods:

  • Developed a minimal time-continuous model for synaptic short-term plasticity.
  • Integrated the model with the spike response neuron model.
  • Derived explicit expressions for synaptic strength based on spike timing.
  • Investigated large, interconnected neural networks.

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Main Results:

  • The model accounts for both short-term depression and facilitation.
  • Explicit formulas for synaptic strength as a function of spike arrival times were derived.
  • Complex transient network behaviors, including various modes of coherent firing, were observed.
  • The existence and stability of limit cycles with coherently firing neurons were elucidated.

Conclusions:

  • The developed model provides a comprehensive framework for studying STP.
  • Short-term synaptic plasticity can lead to complex, evolving network dynamics.
  • Stable, coherent firing patterns emerge in networks with STP after initial stimuli.