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Hierarchical dynamics of informational patterns and decision-making
Pablo Varona1, Mikhail I Rabinovich2
1Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain pablo.varona@uam.es.
Proceedings. Biological Sciences
|June 3, 2016
Summary
This study introduces a new model of dynamic cognitive networks to understand brain function. It reveals how metastable dynamics in these networks explain complex cognitive processes and can indicate creativity or psychiatric disorders.
Area of Science:
- Neuroscience
- Cognitive Science
- Complex Systems
Background:
- Traditional functional connectome studies analyze static brain network connectivity.
- Emerging evidence highlights the dynamic nature of brain networks and spontaneous communication.
- Understanding cognitive function requires analyzing the dynamic interactions between brain networks.
Purpose of the Study:
- To investigate the dynamics of hierarchical networks in cognitive space.
- To model complex cognitive processes, such as decision-making and memory recall, using network dynamics.
- To explore the relationship between network dynamics and cognitive states like creativity and psychiatric disorders.
Main Methods:
- Developed a cognitive model describing metastable pattern dynamics.
- Utilized mathematical representations of hierarchical heteroclinic networks in phase space.
- Applied the model to analyze behavioral decision-making based on sequential episodic memory.
Main Results:
- Demonstrated how metastable pattern dynamics underlie basic cognitive processes.
- Showed that these dynamics can predict complex processes like memory recall and decision strategy competition.
- Identified the robustness of hierarchical heteroclinic network dynamics.
Conclusions:
- The dynamic network approach offers a novel framework for understanding cognitive functions.
- Instabilities in network dynamics may serve as 'dynamical signatures' for creativity and psychiatric disorders.
- This approach provides insights into the dynamical basis of consciousness.
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