Synaptic dependence of dynamic regimes when coupling neural populations

Area of Science:

• Computational Neuroscience
• Dynamical Systems Theory

Background:

• Neural mass models are crucial for understanding large-scale brain activity.
• Next-generation models offer more detailed insights into neural population dynamics.

Purpose of the Study:

• To analyze and compare two coupled neural mass models.
• To investigate the impact of synaptic adaptation on collective neural dynamics.
• To identify distinct dynamical regimes in neural networks.

Main Methods:

• Analysis of all-to-all coupled networks of quadratic integrate-and-fire spiking neurons.
• Mathematical modeling to relate two neural mass models via a parameter.
• Parameter variation to study transitions between dynamical regimes.

Main Results:

• Identified three primary dynamical regimes: Rössler-type (funnel), bursting-type, and spiking-like (oscillator-type).
• Demonstrated a relationship between the two models, allowing for parameter-driven transitions.
• Observed the potential for chaotic collective dynamics with weak synaptic adaptation.

Conclusions:

• The study highlights diverse collective dynamics in neural networks.
• It raises questions about the most suitable dynamical regime for realistic neural simulations.
• Synaptic adaptation plays a critical role in shaping network behavior and the emergence of chaos.