Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters

Area of Science:

• Computational neuroscience
• Artificial intelligence
• Network dynamics

Background:

• Boolean recurrent neural networks (BRNNs) offer a framework for studying complex systems.
• Attractor dynamics in BRNNs are crucial for understanding system behavior.
• The basal ganglia-thalamocortical circuit is a key network for motor control and cognition.

Purpose of the Study:

• To apply the framework of BRNN attractor dynamics to a simplified model of the basal ganglia-thalamocortical circuit.
• To investigate how control parameters influence network complexity and stability.
• To explore the potential of this modeling approach for understanding brain circuit function.

Main Methods:

• Utilizing a directed graph to represent neuronal nodes in the basal ganglia-thalamocortical circuit.
• Implementing control parameters including neuronal excitability and adaptive plasticity.
• Analyzing network dynamics to observe transitions between stable domains and limit cycles.

Main Results:

• Demonstrated that control parameters can induce switches between distinct stable domains.
• Observed highly discontinuous boundaries between these domains.
• Achieved very high levels of network complexity with specific parameter configurations.

Conclusions:

• The BRNN attractor dynamics framework is applicable to modeling brain circuits like the basal ganglia-thalamocortical system.
• Network complexity and stability can be modulated through parameters like excitability and plasticity.
• This approach provides a valuable tool for studying brain circuit dynamics and function.