Short-term synaptic plasticity in the deterministic Tsodyks-Markram model leads to unpredictable network dynamics.

Area of Science:

• Computational Neuroscience
• Systems Neuroscience
• Theoretical Neuroscience

Background:

• Short-term synaptic plasticity significantly influences neural dynamics in cortical networks.
• The Tsodyks-Markram (TM) model is a widely accepted framework for simulating short-term synaptic plasticity across various cortical synapse types.

Purpose of the Study:

• To investigate the emergence of chaotic behavior within the Tsodyks-Markram model.
• To identify the role of Shilnikov homoclinic bifurcations in organizing neural network responses.

Main Methods:

• Analysis of the Tsodyks-Markram model to identify routes to chaotic dynamics.
• Investigation of Shilnikov homoclinic bifurcations and their impact on phase space trajectories.
• Examination of deterministic and stochastic/network versions of the TM model.

Main Results:

• A Shilnikov homoclinic bifurcation was identified as a mechanism inducing chaotic behavior in the TM model.
• This bifurcation leads to irregular transient dynamics, making spike timing and count highly sensitive to initial conditions.
• In network models, the bifurcation generates complex spiking patterns and facilitates state transitions (e.g., down-state to periodic orbits).

Conclusions:

• Shilnikov homoclinic bifurcations play a critical role in generating complex and irregular dynamics in neural networks.
• The interplay between deterministic bifurcations and stochastic effects can explain observed complex dynamics in neural systems.
• This finding provides a theoretical framework for understanding variability and unpredictability in neural activity.