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Related Experiment Videos

Synaptic plasticity with discrete state synapses.

Henry D I Abarbanel1, Sachin S Talathi, Leif Gibb

  • 1Department of Physics and Marine Physical Laboratory (Scripps Institution of Oceanography) University of California, San Diego, La Jolla, CA 92093-0402, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 26, 2005
PubMed
Summary

Synaptic plasticity transitions occur in discrete levels at individual synapses. A new model explains these changes using kinases and phosphatases, enhancing neural synchronization and naturally saturating synaptic strength.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Molecular Neuroscience

Background:

  • Synaptic plasticity, the ability of synapses to strengthen or weaken over time, is crucial for learning and memory.
  • Experimental observations suggest synaptic strength changes occur in discrete levels at individual synapses.
  • Existing models often require artificial cutoffs for synaptic strength.

Purpose of the Study:

  • To develop a quantitative model of a three-state synaptic plasticity system.
  • To investigate the role of kinases and phosphatases in mediating synaptic strength transitions.
  • To explore the impact of this discrete-state plasticity model on neural synchronization.

Main Methods:

  • Developed a quantitative model of a three-state synaptic plasticity system.

Related Experiment Videos

  • Incorporated competition between kinases and phosphatases to determine state transition rates.
  • Coupled discrete alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor conductance changes to postsynaptic membrane potential and calcium fluxes.
  • Simulated long-term potentiation (LTP) and long-term depression (LTD) induction protocols.
  • Examined the effect on synchronization of realistic oscillating neurons.

    • Main Results:

    • The model successfully reproduces discrete synaptic strength transitions for both LTP and LTD.
    • Synaptic strength naturally saturates without artificial cutoffs.
    • The discrete-state plasticity model enhances one-to-one synchronization of oscillating neurons when presynaptic and postsynaptic oscillations are in phase.

    Conclusions:

    • A three-state model driven by kinase-phosphatase competition provides a robust explanation for discrete synaptic plasticity.
    • This model offers a more biologically realistic representation of synaptic strength dynamics.
    • The model's dynamics enhance neural synchrony, suggesting a functional role in network oscillations.