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Robust rhythmogenesis via spike-timing-dependent plasticity.

Gabi Socolovsky1,2, Maoz Shamir1,2,3

  • 1Department of Physics, Faculty of Natural Sciences, Ben-Gurion University of the Negev, Be'er-Sheva 8410501, Israel.

Physical Review. E
|September 16, 2021
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Summary
This summary is machine-generated.

Spike-timing-dependent plasticity (STDP) may tune neural network connectivity for rhythmic activity. This study shows how STDP rules and parameters govern rhythm generation in neuronal networks.

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Neuroplasticity

Background:

  • Rhythmic neural activity is observed across species and linked to cognitive functions.
  • Theoretical models often assume fine-tuned network connectivity for oscillations.
  • The mechanism for achieving this fine-tuning remains unclear.

Purpose of the Study:

  • Investigate spike-timing-dependent plasticity (STDP) as a mechanism for tuning synaptic connectivity.
  • Determine how STDP rules and parameters influence the generation of rhythmic activity.
  • Propose a robust mechanism for self-developing processes, particularly rhythmogenesis.

Main Methods:

  • Modeling study of neuronal networks with excitatory and inhibitory populations.
  • Analysis of STDP dynamics using mean-field Fokker-Planck equations under slow learning.
  • Examination of parameter influence on rhythmic activity generation.

Main Results:

  • Identified specific STDP rules that drive neural networks towards rhythmic activity.
  • Demonstrated that plasticity rule parameters govern the characteristics of rhythmic activity.
  • Showcased the potential of STDP to self-tune network connectivity for oscillations.

Conclusions:

  • STDP offers a plausible biological mechanism for achieving the fine-tuning required for neural rhythmogenesis.
  • The parameters of STDP rules play a critical role in governing oscillatory dynamics.
  • This work provides insights into the self-organizing principles underlying neural network function.