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Items: 5

1.

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data.

Romagnoni A, J├ęgou S, Van Steen K, Wainrib G, Hugot JP; International Inflammatory Bowel Disease Genetics Consortium (IIBDGC).

Sci Rep. 2019 Jul 17;9(1):10351. doi: 10.1038/s41598-019-46649-z.

2.

Biologically Realistic Mean-Field Models of Conductance-Based Networks of Spiking Neurons with Adaptation.

Volo MD, Romagnoni A, Capone C, Destexhe A.

Neural Comput. 2019 Apr;31(4):653-680. doi: 10.1162/neco_a_01173. Epub 2019 Feb 14.

PMID:
30764741
3.

On the Dynamical Interplay of Positive and Negative Affects.

Touboul J, Romagnoni A, Schwartz R.

Neural Comput. 2017 Apr;29(4):897-936. doi: 10.1162/NECO_a_00942. Epub 2017 Feb 9.

PMID:
28181875
4.

Pinwheel-dipole configuration in cat early visual cortex.

Ribot J, Romagnoni A, Milleret C, Bennequin D, Touboul J.

Neuroimage. 2016 Mar;128:63-73. doi: 10.1016/j.neuroimage.2015.12.022. Epub 2015 Dec 17.

PMID:
26707892
5.

Parsimony, Exhaustivity and Balanced Detection in Neocortex.

Romagnoni A, Ribot J, Bennequin D, Touboul J.

PLoS Comput Biol. 2015 Nov 20;11(11):e1004623. doi: 10.1371/journal.pcbi.1004623. eCollection 2015 Nov.

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