PortalPublikationenTracing the Flow of Perceptual Features in an Algorithmic Br...

Tracing the Flow of Perceptual Features in an Algorithmic Brain Network

Standard

Tracing the Flow of Perceptual Features in an Algorithmic Brain Network. / Ince, Robin A A; van Rijsbergen, Nicola J; Thut, Gregor; Rousselet, Guillaume A; Gross, Joachim; Panzeri, Stefano; Schyns, Philippe G.

in: SCI REP-UK, Jahrgang 5, 04.12.2015, S. 17681.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Ince, RAA, van Rijsbergen, NJ, Thut, G, Rousselet, GA, Gross, J, Panzeri, S & Schyns, PG 2015, 'Tracing the Flow of Perceptual Features in an Algorithmic Brain Network', SCI REP-UK, Jg. 5, S. 17681. https://doi.org/10.1038/srep17681

APA

Ince, R. A. A., van Rijsbergen, N. J., Thut, G., Rousselet, G. A., Gross, J., Panzeri, S., & Schyns, P. G. (2015). Tracing the Flow of Perceptual Features in an Algorithmic Brain Network. SCI REP-UK, 5, 17681. https://doi.org/10.1038/srep17681

Vancouver

Ince RAA, van Rijsbergen NJ, Thut G, Rousselet GA, Gross J, Panzeri S et al. Tracing the Flow of Perceptual Features in an Algorithmic Brain Network. SCI REP-UK. 2015 Dez 4;5:17681. https://doi.org/10.1038/srep17681

Bibtex

@article{5744176536194f6ea518554d825ad150,
title = "Tracing the Flow of Perceptual Features in an Algorithmic Brain Network",
abstract = "The model of the brain as an information processing machine is a profound hypothesis in which neuroscience, psychology and theory of computation are now deeply rooted. Modern neuroscience aims to model the brain as a network of densely interconnected functional nodes. However, to model the dynamic information processing mechanisms of perception and cognition, it is imperative to understand brain networks at an algorithmic level--i.e. as the information flow that network nodes code and communicate. Here, using innovative methods (Directed Feature Information), we reconstructed examples of possible algorithmic brain networks that code and communicate the specific features underlying two distinct perceptions of the same ambiguous picture. In each observer, we identified a network architecture comprising one occipito-temporal hub where the features underlying both perceptual decisions dynamically converge. Our focus on detailed information flow represents an important step towards a new brain algorithmics to model the mechanisms of perception and cognition.",
keywords = "Algorithms, Brain/physiology, Brain Mapping, Cognition/physiology, Humans, Models, Neurological, Nerve Net/physiology, Perception/physiology",
author = "Ince, {Robin A A} and {van Rijsbergen}, {Nicola J} and Gregor Thut and Rousselet, {Guillaume A} and Joachim Gross and Stefano Panzeri and Schyns, {Philippe G}",
year = "2015",
month = dec,
day = "4",
doi = "10.1038/srep17681",
language = "English",
volume = "5",
pages = "17681",
journal = "SCI REP-UK",
issn = "2045-2322",
publisher = "NATURE PUBLISHING GROUP",

}

RIS

TY - JOUR

T1 - Tracing the Flow of Perceptual Features in an Algorithmic Brain Network

AU - Ince, Robin A A

AU - van Rijsbergen, Nicola J

AU - Thut, Gregor

AU - Rousselet, Guillaume A

AU - Gross, Joachim

AU - Panzeri, Stefano

AU - Schyns, Philippe G

PY - 2015/12/4

Y1 - 2015/12/4

N2 - The model of the brain as an information processing machine is a profound hypothesis in which neuroscience, psychology and theory of computation are now deeply rooted. Modern neuroscience aims to model the brain as a network of densely interconnected functional nodes. However, to model the dynamic information processing mechanisms of perception and cognition, it is imperative to understand brain networks at an algorithmic level--i.e. as the information flow that network nodes code and communicate. Here, using innovative methods (Directed Feature Information), we reconstructed examples of possible algorithmic brain networks that code and communicate the specific features underlying two distinct perceptions of the same ambiguous picture. In each observer, we identified a network architecture comprising one occipito-temporal hub where the features underlying both perceptual decisions dynamically converge. Our focus on detailed information flow represents an important step towards a new brain algorithmics to model the mechanisms of perception and cognition.

AB - The model of the brain as an information processing machine is a profound hypothesis in which neuroscience, psychology and theory of computation are now deeply rooted. Modern neuroscience aims to model the brain as a network of densely interconnected functional nodes. However, to model the dynamic information processing mechanisms of perception and cognition, it is imperative to understand brain networks at an algorithmic level--i.e. as the information flow that network nodes code and communicate. Here, using innovative methods (Directed Feature Information), we reconstructed examples of possible algorithmic brain networks that code and communicate the specific features underlying two distinct perceptions of the same ambiguous picture. In each observer, we identified a network architecture comprising one occipito-temporal hub where the features underlying both perceptual decisions dynamically converge. Our focus on detailed information flow represents an important step towards a new brain algorithmics to model the mechanisms of perception and cognition.

KW - Algorithms

KW - Brain/physiology

KW - Brain Mapping

KW - Cognition/physiology

KW - Humans

KW - Models, Neurological

KW - Nerve Net/physiology

KW - Perception/physiology

U2 - 10.1038/srep17681

DO - 10.1038/srep17681

M3 - SCORING: Journal article

C2 - 26635299

VL - 5

SP - 17681

JO - SCI REP-UK

JF - SCI REP-UK

SN - 2045-2322

ER -