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Haim Sompolinsky

Hebrew University, Jerusalem, Jerusalem, Israel 
"Haim Sompolinsky"
Mean distance: 12.83 (cluster 17)
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Cohen U, Chung S, Lee DD, et al. (2020) Separability and geometry of object manifolds in deep neural networks. Nature Communications. 11: 746
Gjorgjieva J, Meister M, Sompolinsky H. (2019) Functional diversity among sensory neurons from efficient coding principles. Plos Computational Biology. 15: e1007476
Chung S, Cohen U, Sompolinsky H, et al. (2018) Learning Data Manifolds with a Cutting Plane Method. Neural Computation. 1-23
Rubin R, Abbott LF, Sompolinsky H. (2017) Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity. Proceedings of the National Academy of Sciences of the United States of America
Litwin-Kumar A, Harris KD, Axel R, et al. (2017) Optimal Degrees of Synaptic Connectivity. Neuron
Chung S, Lee DD, Sompolinsky H. (2016) Linear readout of object manifolds. Physical Review. E. 93: 060301
Furstenberg A, Breska A, Sompolinsky H, et al. (2015) Evidence of Change of Intention in Picking Situations. Journal of Cognitive Neuroscience. 1-14
Kadmon J, Sompolinsky H. (2015) Transition to chaos in random neuronal networks Physical Review X. 5
Stern M, Sompolinsky H, Abbott LF. (2014) Dynamics of random neural networks with bistable units. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 90: 062710
Gjorgjieva J, Sompolinsky H, Meister M. (2014) Benefits of pathway splitting in sensory coding. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 34: 12127-44
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