Modeling effects of neural fluctuations and inter-scale interactions

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Understanding the relationship between different organizational levels in neural systems is a key scientific challenge.
• The functional significance of neural fluctuations, noise, and chaos remains an area of active investigation.

Purpose of the Study:

• To explore the integration of processes across micro (molecular), meso (cellular), and macro (network) scales in neural systems.
• To investigate the functional significance of non-linear neurodynamics, including oscillations, chaos, and noise.
• To demonstrate the utility of computational models in bridging different levels of neural organization.

Main Methods:

• Utilizing complex computational models tailored to experimental data across multiple scales (molecular, cellular, network).
• Examining models developed by Hans Braun and colleagues, alongside models from the author's group.
• Analyzing both upward and downward causation within neural systems.

Main Results:

• Computational models can effectively bridge different organizational levels of neural systems.
• Noise and chaos play constructive roles in neural functions such as sensation, perception, learning, memory, and decision-making.
• Downward causation is explored as a necessary condition for consciousness and free will.

Conclusions:

• Computational modeling is crucial for integrating multi-scale neural data and understanding complex brain functions.
• Non-linear dynamics, including noise and chaos, are functionally significant in neural processing.
• Addressing downward causation is essential for a comprehensive scientific understanding of consciousness and free will.