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Shaping the Default Activity Pattern of the Cortical Network.

Maria V Sanchez-Vives¹, Marcello Massimini², Maurizio Mattia³

¹Systems Neuroscience, IDIBAPS, 08036 Barcelona, Spain; ICREA, 08010 Barcelona, Spain.

|June 9, 2017

Summary

This summary is machine-generated.

Slow oscillations represent the brain's default activity, reflecting network health. Emerging from physiological variations, this state is crucial for understanding cortical function and recovering consciousness.

Keywords:

attractors complexity consciousness homeostasis slow oscillations slow waves unconsciousness

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Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Slow oscillations are proposed as the default emergent activity of the cortical network.
This low-complexity state integrates neuronal, synaptic, and connectivity properties.
Variations in physiological parameters shape slow oscillations, offering insights into network health or pathology.

Purpose of the Study:

To review how the brain's default slow oscillation activity is shaped.
To examine the role of slow oscillations as a powerful attractor state.
To explore the necessity of exiting slow oscillations for regaining complex conscious states.

Main Methods:

Review of existing literature on cortical network activity.
Analysis of the properties and dynamics of slow oscillations.
Theoretical exploration of network complexity and conscious states.

Main Results:

Slow oscillations are a fundamental property of the cortical network, integrating diverse neural characteristics.
The characteristics of slow oscillations provide a window into the healthy or pathological state of the cortex.
Transitioning out of the slow oscillation state is essential for restoring the neural complexity associated with consciousness.

Conclusions:

Slow oscillations serve as a unifying paradigm for studying cortical function.
Understanding slow oscillations is key to deciphering brain states, from default activity to consciousness.
This framework aids in diagnosing and potentially treating network pathologies affecting cognitive function.