Gal Chechik

Affiliations: 
Stanford University, Palo Alto, CA 
 Gonda Brain Research Center Bar-Ilan University, Jerusalem, Jerusalem District, Israel 
Area:
Computational Neuroscience
Website:
http://chechiklab.biu.ac.il/
Google:
"Gal Chechik"
Mean distance: 15.63 (cluster 17)
 
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Publications

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Podvalny E, Yeagle E, Mégevand P, et al. (2016) Invariant Temporal Dynamics Underlie Perceptual Stability in Human Visual Cortex. Current Biology : Cb
Kirsch L, Chechik G. (2016) On Expression Patterns and Developmental Origin of Human Brain Regions. Plos Computational Biology. 12: e1005064
Beker S, Kellner V, Chechik G, et al. (2016) Learning to classify neural activity from a mouse model of Alzheimer's disease amyloidosis versus controls. Alzheimer's & Dementia (Amsterdam, Netherlands). 2: 39-48
Bar-Shira O, Maor R, Chechik G. (2015) Gene Expression Switching of Receptor Subunits in Human Brain Development. Plos Computational Biology. 11: e1004559
Podvalny E, Noy N, Harel M, et al. (2015) A unifying principle underlying the extracellular field potential spectral responses in the human cortex. Journal of Neurophysiology. 114: 505-19
Lakretz Y, Chechik G, Friedmann N, et al. (2015) Probabilistic graphical models of dyslexia Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. 2015: 1919-1928
Liscovitch N, Bazak L, Levanon EY, et al. (2014) Positive correlation between ADAR expression and its targets suggests a complex regulation mediated by RNA editing in the human brain. Rna Biology. 11: 1447-56
Beker S, Goldin M, Menkes-Caspi N, et al. (2014) Amyloid-β disrupts ongoing spontaneous activity in sensory cortex. Brain Structure & Function
Shoval G, Bar-Shira O, Zalsman G, et al. (2014) Transitions in the transcriptome of the serotonergic and dopaminergic systems in the human brain during adolescence. European Neuropsychopharmacology : the Journal of the European College of Neuropsychopharmacology. 24: 1123-32
Mesnil G, Bordes A, Weston J, et al. (2014) Learning semantic representations of objects and their parts Machine Learning. 94: 281-301
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