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  5. A Hierarchy of Time-Scales and the Brain

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Article
English
2008

A Hierarchy of Time-Scales and the Brain

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English
2008
PLoS Computational Biology
Vol 4 (11)
DOI: 10.1371/journal.pcbi.1000209

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Karl Friston
Karl Friston

University College London

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Stefan J. Kiebel
Jean Daunizeau
Karl Friston

Abstract

In this paper, we suggest that cortical anatomy recapitulates the temporal hierarchy that is inherent in the dynamics of environmental states. Many aspects of brain function can be understood in terms of a hierarchy of temporal scales at which representations of the environment evolve. The lowest level of this hierarchy corresponds to fast fluctuations associated with sensory processing, whereas the highest levels encode slow contextual changes in the environment, under which faster representations unfold. First, we describe a mathematical model that exploits the temporal structure of fast sensory input to track the slower trajectories of their underlying causes. This model of sensory encoding or perceptual inference establishes a proof of concept that slowly changing neuronal states can encode the paths or trajectories of faster sensory states. We then review empirical evidence that suggests that a temporal hierarchy is recapitulated in the macroscopic organization of the cortex. This anatomic-temporal hierarchy provides a comprehensive framework for understanding cortical function: the specific time-scale that engages a cortical area can be inferred by its location along a rostro-caudal gradient, which reflects the anatomical distance from primary sensory areas. This is most evident in the prefrontal cortex, where complex functions can be explained as operations on representations of the environment that change slowly. The framework provides predictions about, and principled constraints on, cortical structure-function relationships, which can be tested by manipulating the time-scales of sensory input.

How to cite this publication

Stefan J. Kiebel, Jean Daunizeau, Karl Friston (2008). A Hierarchy of Time-Scales and the Brain. PLoS Computational Biology, 4(11), pp. e1000209-e1000209, DOI: 10.1371/journal.pcbi.1000209.

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Publication Details

Type

Article

Year

2008

Authors

3

Datasets

0

Total Files

0

Language

English

Journal

PLoS Computational Biology

DOI

10.1371/journal.pcbi.1000209

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