Low dimensional criticality embedded in high dimensional awake brain dynamics.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Dynamical Systems

Background:

• The debate on whether cortical neurons operate in strongly or weakly correlated dynamical regimes impacts understanding of brain information processing.
• Two key dynamical regimes, criticality and desynchronization, have been considered incompatible within local cortical circuits.

Approach:

• Investigated neural dynamics in awake mouse motor cortex using dimensionality reduction techniques.
• Analyzed neural activity across different timescales to identify distinct dynamical subspaces.
• Employed a computational model to validate theoretical predictions of critical dynamics.

Key Points:

• Identified a low-dimensional critical subspace with large fluctuations, supporting moderate correlations and multi-scale information capacity.
• Discovered a high-dimensional, desynchronized subspace orthogonal to the critical one, potentially optimizing input discrimination.
• Observed that the critical subspace is prominent at longer timescales, resolving previous study discrepancies.

Conclusions:

• Cortical circuits can multiplex distinct dynamical regimes (criticality and desynchronization) across orthogonal subspaces.
• The brain leverages high dimensionality to segregate and optimize different information processing functions.
• Findings reconcile long-standing debates on neural dynamics and offer insights into neural computation.