Elad Ganmor
Elad Ganmor
Email verificata su google.com
Citata da
Citata da
Sparse low-order interaction network underlies a highly correlated and learnable neural population code
E Ganmor, R Segev, E Schneidman
Proceedings of the National Academy of sciences 108 (23), 9679-9684, 2011
Shift in the balance between excitation and inhibition during sensory adaptation of S1 neurons
JE Heiss, Y Katz, E Ganmor, I Lampl
Journal of Neuroscience 28 (49), 13320-13330, 2008
Near-optimal integration of orientation information across saccades
E Ganmor, MS Landy, EP Simoncelli
Journal of vision 15 (16), 8-8, 2015
The architecture of functional interaction networks in the retina
E Ganmor, R Segev, E Schneidman
Journal of Neuroscience 31 (8), 3044-3054, 2011
Intensity-dependent adaptation of cortical and thalamic neurons is controlled by brainstem circuits of the sensory pathway
E Ganmor, Y Katz, I Lampl
Neuron 66 (2), 273-286, 2010
A thesaurus for a neural population code
E Ganmor, R Segev, E Schneidman
Elife 4, e06134, 2015
Direct estimation of firing rates from calcium imaging data
E Ganmor, M Krumin, LF Rossi, M Carandini, EP Simoncelli
arXiv preprint arXiv:1601.00364, 2016
Faithful representation of tactile intensity under different contexts emerges from the distinct adaptive properties of the first somatosensory relay stations
B Mohar, E Ganmor, I Lampl
Journal of Neuroscience 35 (18), 6997-7002, 2015
How fast can we learn maximum entropy models of neural populations
E Ganmor, R Segev, E Schneidman
J Phys Conf Ser 197, 012020, 2009
Movable user interface
K Kinerk, D Wong
US Patent App. 10/334,057, 2004
The V1 population gains normalization
E Ganmor, M Okun, I Lampl
Neuron 64 (6), 778-780, 2009
Pattern based optimization of digital component transmission
E Christophe, E Ganmor, QP Lau
US Patent 10,334,057, 2019
Structure and Robustness of 2 nd Order Maximum Entropy Models for Large Neural Populations
E Ganmor, R Segev, E Schneidman
Il sistema al momento non pu eseguire l'operazione. Riprova pi tardi.
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