Zen and the Art of Making a Bayesian Espresso

Lei Zhang; Saša Redžepović; Michael Rose; Jan Gläscher

doi:10.1016/j.neuron.2018.06.023

Zen and the Art of Making a Bayesian Espresso

Lei Zhang
, Saša Redžepović
, Michael Rose
, Jan Gläscher

Research output: Contribution to journal › Letter › peer-review

Abstract

In this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure. In this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure.

Original language	English
Pages (from-to)	1066-1068
Number of pages	3
Journal	Neuron
Volume	98
Issue number	6
DOIs	https://doi.org/10.1016/j.neuron.2018.06.023
Publication status	Published - 27 Jun 2018

Keywords

501011 Cognitive psychology
501011 Kognitionspsychologie
301407 Neurophysiology
301407 Neurophysiologie

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10.1016/j.neuron.2018.06.023

Cite this

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title = "Zen and the Art of Making a Bayesian Espresso",

abstract = "In this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure. In this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure.",

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year = "2018",

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AU - Zhang, Lei

AU - Redžepović, Saša

AU - Rose, Michael

AU - Gläscher, Jan

PY - 2018/6/27

Y1 - 2018/6/27

N2 - In this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure. In this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure.

AB - In this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure. In this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure.

KW - 501011 Cognitive psychology

KW - 501011 Kognitionspsychologie

KW - 301407 Neurophysiology

KW - 301407 Neurophysiologie

U2 - 10.1016/j.neuron.2018.06.023

DO - 10.1016/j.neuron.2018.06.023

M3 - Letter

SN - 0896-6273

VL - 98

SP - 1066

EP - 1068

JO - Neuron

JF - Neuron

IS - 6

ER -

Zen and the Art of Making a Bayesian Espresso

Abstract

Keywords

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