| 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 | 284 | 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 | 148 | 2008 |
| Near-optimal integration of orientation information across saccades E Ganmor, MS Landy, EP Simoncelli Journal of vision 15 (16), 8-8, 2015 | 129 | 2015 |
| The architecture of functional interaction networks in the retina E Ganmor, R Segev, E Schneidman Journal of Neuroscience 31 (8), 3044-3054, 2011 | 114 | 2011 |
| A thesaurus for a neural population code E Ganmor, R Segev, E Schneidman Elife 4, e06134, 2015 | 68 | 2015 |
| 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 | 63 | 2010 |
| 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 | 20 | 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 | 16 | 2015 |
| How fast can we learn maximum entropy models of neural populations? E Ganmor, R Segev, E Schneidman Journal of Physics: Conference Series 197 (1), 012020, 2009 | 10 | 2009 |
| The V1 population gains normalization E Ganmor, M Okun, I Lampl Neuron 64 (6), 778-780, 2009 | 2 | 2009 |
| Pattern based optimization of digital component transmission E Christophe, E Ganmor, QP Lau US Patent 10,334,057, 2019 | | 2019 |
| Structure and Robustness of 2 nd Order Maximum Entropy Models for Large Neural Populations E Ganmor, R Segev, E Schneidman | | |
