Dynamic geometry, brain function modeling, and consciousness

Area of Science:

• Neuroscience
• Computational Geometry
• Theoretical Physics

Background:

• The central nervous system (CNS) constructs internal models of the external world via sensory-motor interactions.
• Tensor network theory offers a computational approach to brain function, emphasizing prediction at the neuronal level.
• Evolutionary development of internal functional spaces allows interaction with external reality.

Purpose of the Study:

• To investigate the role of dynamic geometry in brain function modeling.
• To explore the neuronal basis of consciousness through a dynamic geometry framework.
• To elucidate how dynamic geometry accounts for individual distinctions in brain function.

Main Methods:

• Utilizing tensor network theory for computational modeling of brain function.
• Developing a dynamic geometry model incorporating stochastic metric tensor properties.
• Analyzing the significance of 40 Hz neuronal oscillations and their role in perception and simultaneity.

Main Results:

• Dynamic geometry proposes a five-dimensional space-time model for the brain, with the fifth dimension representing probability and metric space.
• This framework links external world events to internal representations via stochastic metric tensors.
• The model highlights the role of 40 Hz oscillations in recognizing external events and consciousness, differentiating between awake and dream states.

Conclusions:

• Dynamic geometry provides a novel framework for understanding brain function and the neuronal basis of consciousness.
• The model's stochastic metric tensor properties and five-dimensional space-time offer insights into internal world modeling.
• This approach has the potential to explain individual differences in cognitive processes and consciousness.