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

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.