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Items: 17

1.

Helicobacter pylori and non-alcoholic fatty liver disease: A new enigma?

Abdel-Razik A, Mousa N, Shabana W, Refaey M, Elhelaly R, Elzehery R, Abdelsalam M, Elgamal A, Nassar MR, Abu El-Soud A, Seif AS, Tawfik AM, El-Wakeel N, Eldars W.

Helicobacter. 2018 Dec;23(6):e12537. doi: 10.1111/hel.12537. Epub 2018 Sep 23.

PMID:
30246507
2.

Chunking as a rational strategy for lossy data compression in visual working memory.

Nassar MR, Helmers JC, Frank MJ.

Psychol Rev. 2018 Jul;125(4):486-511. doi: 10.1037/rev0000101.

PMID:
29952621
3.

Correction: A Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems.

Wilson RC, Nassar MR, Tavoni G, Gold JI.

PLoS Comput Biol. 2018 Jun 26;14(6):e1006210. doi: 10.1371/journal.pcbi.1006210. eCollection 2018 Jun.

4.

A Control Theoretic Model of Adaptive Learning in Dynamic Environments.

Ritz H, Nassar MR, Frank MJ, Shenhav A.

J Cogn Neurosci. 2018 Oct;30(10):1405-1421. doi: 10.1162/jocn_a_01289. Epub 2018 Jun 7.

PMID:
29877769
5.

Computational neuroscience across the lifespan: Promises and pitfalls.

van den Bos W, Bruckner R, Nassar MR, Mata R, Eppinger B.

Dev Cogn Neurosci. 2018 Oct;33:42-53. doi: 10.1016/j.dcn.2017.09.008. Epub 2017 Oct 13. Review.

6.

Arousal-related adjustments of perceptual biases optimize perception in dynamic environments.

Krishnamurthy K, Nassar MR, Sarode S, Gold JI.

Nat Hum Behav. 2017;1. pii: 0107. doi: 10.1038/s41562-017-0107. Epub 2017 May 8.

7.

Catecholaminergic Regulation of Learning Rate in a Dynamic Environment.

Jepma M, Murphy PR, Nassar MR, Rangel-Gomez M, Meeter M, Nieuwenhuis S.

PLoS Comput Biol. 2016 Oct 28;12(10):e1005171. doi: 10.1371/journal.pcbi.1005171. eCollection 2016 Oct.

8.
9.

Age differences in learning emerge from an insufficient representation of uncertainty in older adults.

Nassar MR, Bruckner R, Gold JI, Li SC, Heekeren HR, Eppinger B.

Nat Commun. 2016 Jun 10;7:11609. doi: 10.1038/ncomms11609.

10.

What do we GANE with age?

Nassar MR, Bruckner R, Eppinger B.

Behav Brain Sci. 2016 Jan;39:e218. doi: 10.1017/S0140525X15001892.

PMID:
28347395
11.

The mitochondrial uncoupler DNP triggers brain cell mTOR signaling network reprogramming and CREB pathway up-regulation.

Liu D, Zhang Y, Gharavi R, Park HR, Lee J, Siddiqui S, Telljohann R, Nassar MR, Cutler RG, Becker KG, Mattson MP.

J Neurochem. 2015 Aug;134(4):677-92. doi: 10.1111/jnc.13176. Epub 2015 Jun 19.

12.

Functionally dissociable influences on learning rate in a dynamic environment.

McGuire JT, Nassar MR, Gold JI, Kable JW.

Neuron. 2014 Nov 19;84(4):870-81. doi: 10.1016/j.neuron.2014.10.013. Epub 2014 Nov 19.

13.

A mixture of delta-rules approximation to bayesian inference in change-point problems.

Wilson RC, Nassar MR, Gold JI.

PLoS Comput Biol. 2013;9(7):e1003150. doi: 10.1371/journal.pcbi.1003150. Epub 2013 Jul 25. Erratum in: PLoS Comput Biol. 2018 Jun 26;14(6):e1006210.

14.

A healthy fear of the unknown: perspectives on the interpretation of parameter fits from computational models in neuroscience.

Nassar MR, Gold JI.

PLoS Comput Biol. 2013 Apr;9(4):e1003015. doi: 10.1371/journal.pcbi.1003015. Epub 2013 Apr 4.

15.

Rational regulation of learning dynamics by pupil-linked arousal systems.

Nassar MR, Rumsey KM, Wilson RC, Parikh K, Heasly B, Gold JI.

Nat Neurosci. 2012 Jun 3;15(7):1040-6. doi: 10.1038/nn.3130.

16.

An approximately Bayesian delta-rule model explains the dynamics of belief updating in a changing environment.

Nassar MR, Wilson RC, Heasly B, Gold JI.

J Neurosci. 2010 Sep 15;30(37):12366-78. doi: 10.1523/JNEUROSCI.0822-10.2010.

17.

Bayesian online learning of the hazard rate in change-point problems.

Wilson RC, Nassar MR, Gold JI.

Neural Comput. 2010 Sep 1;22(9):2452-76. doi: 10.1162/NECO_a_00007.

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