Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Hierarchy of information processing in the brain: A novel ‘intrinsic ignition’ framework

MPG-Autoren
/persons/resource/persons208989

Deco,  Gustavo
Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Spain;
Catalan Institution for Research and Advanced Studies (ICREA), University Pompeu Fabra, Barcelona, Spain;
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
School of Psychological Sciences, Monash University, Melbourne, Australia;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Deco, G., & Kringelbach, M. L. (2017). Hierarchy of information processing in the brain: A novel ‘intrinsic ignition’ framework. Neuron, 94(5), 961-968. doi:10.1016/j.neuron.2017.03.028.


Zitierlink: https://hdl.handle.net/21.11116/0000-0004-A901-C
Zusammenfassung
A general theory of brain function has to be able to explain local and non-local network computations over space and time. We propose a new framework to capture the key principles of how local activity influences global computation, i.e., describing the propagation of information and thus the broadness of communication driven by local activity. More specifically, we consider the diversity in space (nodes or brain regions) over time using the concept of intrinsic ignition, which are naturally occurring intrinsic perturbations reflecting the capability of a given brain area to propagate neuronal activity to other regions in a given brain state. Characterizing the profile of intrinsic ignition for a given brain state provides insight into the precise nature of hierarchical information processing. Combining this data-driven method with a causal whole-brain computational model can provide novel insights into the imbalance of brain states found in neuropsychiatric disorders.