#32 · Systems biology
Criticality, attractor dynamics and decoding probe large-scale brain dynamics
The dominant thread is quantitative characterization of brain dynamics across scales and modalities, beyond static pairwise correlation. Resting-state human MEG coarse-graining reports power-law scaling and avalanche invariance, with exponents tied to a criticality framework. A whole-cortex mouse network with directed Allen-atlas connectivity reproduces fMRI co-activation patterns through attractor dynamics. A separate rat EEG study applies an asynchronous recurrent decoder to predict self-paced locomotion speed (r=0.88) from 32 skull electrodes, addressing behavioral readout rather than model structure. These items do not test a shared model. What is new is the pairing of scale-dependent, dynamical and decoding descriptors across human MEG, rodent EEG and mouse fMRI.
Published papers
Attractor dynamics of a whole-cortex network model predicts emergence and structure of fMRI co-activation patterns in the mouse brain.
Whole-cortex mouse network model with directed Allen-atlas connectivity reproduces fMRI co-activation patterns via attractor dynamics; fiber directionality and…
Preprints
Scaling and tuning to criticality in resting-state human magnetoencephalography
Applies an RG-inspired coarse-graining analysis to resting-state human MEG, finding power-law scaling and avalanche invariance, and links exponents to…
Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks
Introduces an asynchronous RNN-based EEG decoder that predicts self-paced rat locomotion speed (r=0.88) from 32 skull electrodes.
Topic history
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