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Distributed Synaptic Connection Strength Changes Dynamics in a Population Firing Rate Model in Response to Continuous

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Neural network complexity, including synaptic plasticity, influences brain functions. This study models synaptic variations to understand network synchronization, revealing insights into brain oscillations.

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Area of Science:

  • Computational Neuroscience
  • Neuroscience

Background:

  • Neural network complexity supports cognition and memory.
  • Synaptic plasticity enhances complexity but its effect on network synchronization is unclear in macroscopic models.

Purpose of the Study:

  • To incorporate synaptic conductance and connection strength variation into neuronal population models.
  • To investigate the impact of these variations on network synchronization and firing dynamics.

Main Methods:

  • Developed macroscopic firing rate equations based on mean field theory for quadratic integrate-and-fire networks.
  • Introduced a heuristic switching rule to handle computational divergences from connection strength variations.
  • Validated the model against microscopic level simulations.

Main Results:

  • Variations in synaptic conductance and connection strength significantly affect solution stability and synchronous firing mechanisms.
  • The model reproduced event-related desynchronization (alpha/beta frequencies) and synchronization (gamma frequency) using physiologically plausible values from mammalian visual cortex.
  • Demonstrated that complex synaptic connections and realistic values in low-dimensional models can capture dynamic changes.

Conclusions:

  • The derived mean field model accurately reproduces dynamic changes in neuronal networks, such as event-related (de)synchronization.
  • Provides mathematical insight into how synaptic strength variation influences oscillatory mechanisms in neural populations.