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Locus Coeruleus tracking of prediction errors optimises cognitive flexibility: An Active Inference model

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Locus Coeruleus tracking of prediction errors optimises cognitive flexibility : An Active Inference model. / Sales, Anna C.; Friston, Karl J.; Jones, Matthew W.; Pickering, Anthony E.; Moran, Rosalyn J.

In: PLoS Computational Biology, Vol. 15, No. 1, e1006267, 04.01.2019.

Research output: Contribution to journalArticle

Harvard

Sales, AC, Friston, KJ, Jones, MW, Pickering, AE & Moran, RJ 2019, 'Locus Coeruleus tracking of prediction errors optimises cognitive flexibility: An Active Inference model', PLoS Computational Biology, vol. 15, no. 1, e1006267. https://doi.org/10.1371/journal.pcbi.1006267

APA

Sales, A. C., Friston, K. J., Jones, M. W., Pickering, A. E., & Moran, R. J. (2019). Locus Coeruleus tracking of prediction errors optimises cognitive flexibility: An Active Inference model. PLoS Computational Biology, 15(1), [e1006267]. https://doi.org/10.1371/journal.pcbi.1006267

Vancouver

Sales AC, Friston KJ, Jones MW, Pickering AE, Moran RJ. Locus Coeruleus tracking of prediction errors optimises cognitive flexibility: An Active Inference model. PLoS Computational Biology. 2019 Jan 4;15(1). e1006267. https://doi.org/10.1371/journal.pcbi.1006267

Author

Sales, Anna C. ; Friston, Karl J. ; Jones, Matthew W. ; Pickering, Anthony E. ; Moran, Rosalyn J. / Locus Coeruleus tracking of prediction errors optimises cognitive flexibility : An Active Inference model. In: PLoS Computational Biology. 2019 ; Vol. 15, No. 1.

Bibtex Download

@article{f461efd3b5fc400f9da8c56d8802d066,
title = "Locus Coeruleus tracking of prediction errors optimises cognitive flexibility: An Active Inference model",
abstract = "The locus coeruleus (LC) in the pons is the major source of noradrenaline (NA) in the brain. Two modes of LC firing have been associated with distinct cognitive states: changes in tonic rates of firing are correlated with global levels of arousal and behavioural flexibility, whilst phasic LC responses are evoked by salient stimuli. Here, we unify these two modes of firing by modelling the response of the LC as a correlate of a prediction error when inferring states for action planning under Active Inference (AI). We simulate a classic Go/No-go reward learning task and a three-arm ‘explore/exploit' task and show that, if LC activity is considered to reflect the magnitude of high level ‘state-action' prediction errors, then both tonic and phasic modes of firing are emergent features of belief updating. We also demonstrate that when contingencies change, AI agents can update their internal models more quickly by feeding back this state-action prediction error-reflected in LC firing and noradrenaline release-to optimise learning rate, enabling large adjustments over short timescales. We propose that such prediction errors are mediated by cortico-LC connections, whilst ascending input from LC to cortex modulates belief updating in anterior cingulate cortex (ACC). In short, we characterise the LC/ NA system within a general theory of brain function. In doing so, we show that contrasting, behaviour-dependent firing patterns are an emergent property of the LC that translates state-action prediction errors into an optimal balance between plasticity and stability.",
author = "Sales, {Anna C.} and Friston, {Karl J.} and Jones, {Matthew W.} and Pickering, {Anthony E.} and Moran, {Rosalyn J.}",
year = "2019",
month = "1",
day = "4",
doi = "10.1371/journal.pcbi.1006267",
language = "English",
volume = "15",
journal = "PL o S Computational Biology",
issn = "1553-734X",
number = "1",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Locus Coeruleus tracking of prediction errors optimises cognitive flexibility

T2 - An Active Inference model

AU - Sales, Anna C.

AU - Friston, Karl J.

AU - Jones, Matthew W.

AU - Pickering, Anthony E.

AU - Moran, Rosalyn J.

PY - 2019/1/4

Y1 - 2019/1/4

N2 - The locus coeruleus (LC) in the pons is the major source of noradrenaline (NA) in the brain. Two modes of LC firing have been associated with distinct cognitive states: changes in tonic rates of firing are correlated with global levels of arousal and behavioural flexibility, whilst phasic LC responses are evoked by salient stimuli. Here, we unify these two modes of firing by modelling the response of the LC as a correlate of a prediction error when inferring states for action planning under Active Inference (AI). We simulate a classic Go/No-go reward learning task and a three-arm ‘explore/exploit' task and show that, if LC activity is considered to reflect the magnitude of high level ‘state-action' prediction errors, then both tonic and phasic modes of firing are emergent features of belief updating. We also demonstrate that when contingencies change, AI agents can update their internal models more quickly by feeding back this state-action prediction error-reflected in LC firing and noradrenaline release-to optimise learning rate, enabling large adjustments over short timescales. We propose that such prediction errors are mediated by cortico-LC connections, whilst ascending input from LC to cortex modulates belief updating in anterior cingulate cortex (ACC). In short, we characterise the LC/ NA system within a general theory of brain function. In doing so, we show that contrasting, behaviour-dependent firing patterns are an emergent property of the LC that translates state-action prediction errors into an optimal balance between plasticity and stability.

AB - The locus coeruleus (LC) in the pons is the major source of noradrenaline (NA) in the brain. Two modes of LC firing have been associated with distinct cognitive states: changes in tonic rates of firing are correlated with global levels of arousal and behavioural flexibility, whilst phasic LC responses are evoked by salient stimuli. Here, we unify these two modes of firing by modelling the response of the LC as a correlate of a prediction error when inferring states for action planning under Active Inference (AI). We simulate a classic Go/No-go reward learning task and a three-arm ‘explore/exploit' task and show that, if LC activity is considered to reflect the magnitude of high level ‘state-action' prediction errors, then both tonic and phasic modes of firing are emergent features of belief updating. We also demonstrate that when contingencies change, AI agents can update their internal models more quickly by feeding back this state-action prediction error-reflected in LC firing and noradrenaline release-to optimise learning rate, enabling large adjustments over short timescales. We propose that such prediction errors are mediated by cortico-LC connections, whilst ascending input from LC to cortex modulates belief updating in anterior cingulate cortex (ACC). In short, we characterise the LC/ NA system within a general theory of brain function. In doing so, we show that contrasting, behaviour-dependent firing patterns are an emergent property of the LC that translates state-action prediction errors into an optimal balance between plasticity and stability.

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VL - 15

JO - PL o S Computational Biology

JF - PL o S Computational Biology

SN - 1553-734X

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ER -

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