Hierarchy-informed curricular optimization of heterogeneous whole-brain models enables generalization to new subjects and prediction of behavioral abilities from parameters.
Dynamic models of large-scale brain activity
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Proposes functional whole-brain models defined by four criteria that integrate empirical connectomes, dynamical realism, and task-performing competence across cognitive domains.
Tuning a human connectome model via standardized metrics yields emergent alpha-band oscillations, infra-slow rhythms, and higher perturbational complexity in both spontaneous and evoked regimes.
citing papers explorer
-
Evolution With Purpose: Hierarchy-Informed Optimization of Whole-Brain Models
Hierarchy-informed curricular optimization of heterogeneous whole-brain models enables generalization to new subjects and prediction of behavioral abilities from parameters.
-
Functional Whole-Brain Models: A New Framework for Unifying Brain Structure and Cognitive Function
Proposes functional whole-brain models defined by four criteria that integrate empirical connectomes, dynamical realism, and task-performing competence across cognitive domains.
-
Emergent complexity and rhythms in evoked and spontaneous dynamics of human whole-brain models after tuning through analysis tools
Tuning a human connectome model via standardized metrics yields emergent alpha-band oscillations, infra-slow rhythms, and higher perturbational complexity in both spontaneous and evoked regimes.