Letting the brain speak for itself

Area of Science:

• Theoretical Neuroscience
• Complex Systems Dynamics
• Neural System Function

Background:

• Traditional computational and information metaphors may obscure intrinsic neural processes.
• Observer's role in data collection and interpretation can influence understanding.
• Neural systems exhibit self-referential modes and self-organization towards critical states.

Purpose of the Study:

• To propose a new framework for understanding neural system functions.
• To shift focus from observer-dependent interpretations to intrinsic neural dynamics.
• To highlight the need for advanced mathematical tools in theoretical neuroscience.

Main Methods:

• Adopting a Complex System Dynamics perspective.
• Analyzing non-ergodic and non-stationary processes.
• Utilizing inverse-power-law statistical distributions.
• Applying Fractional Calculus and Random Walk models with long-term memory.

Main Results:

• Identified intrinsic neural modes independent of observer bias.
• Characterized neural self-organization towards critical states.
• Demonstrated the fractal nature of local cooperative neural processes.

Conclusions:

• A paradigm shift is needed, moving beyond computational metaphors.
• Complex System Dynamics offers a more intrinsic view of neural function.
• Fractional Calculus and memory-dependent Random Walks are essential tools for theoretical neuroscience.