| Year |
Citation |
Score |
| 2022 |
Cohen U, Sompolinsky H. Soft-margin classification of object manifolds. Physical Review. E. 106: 024126. PMID 36109959 DOI: 10.1103/PhysRevE.106.024126 |
0.695 |
|
| 2022 |
Hu Y, Sompolinsky H. The spectrum of covariance matrices of randomly connected recurrent neuronal networks with linear dynamics. Plos Computational Biology. 18: e1010327. PMID 35862445 DOI: 10.1371/journal.pcbi.1010327 |
0.317 |
|
| 2021 |
Ginosar G, Aljadeff J, Burak Y, Sompolinsky H, Las L, Ulanovsky N. Locally ordered representation of 3D space in the entorhinal cortex. Nature. PMID 34381211 DOI: 10.1038/s41586-021-03783-x |
0.585 |
|
| 2020 |
Advani MS, Saxe AM, Sompolinsky H. High-dimensional dynamics of generalization error in neural networks. Neural Networks : the Official Journal of the International Neural Network Society. 132: 428-446. PMID 33022471 DOI: 10.1016/j.neunet.2020.08.022 |
0.403 |
|
| 2020 |
Cohen U, Chung S, Lee DD, Sompolinsky H. Separability and geometry of object manifolds in deep neural networks. Nature Communications. 11: 746. PMID 32029727 DOI: 10.1038/S41467-020-14578-5 |
0.78 |
|
| 2019 |
Maor I, Shwartz-Ziv R, Feigin L, Elyada Y, Sompolinsky H, Mizrahi A. Neural Correlates of Learning Pure Tones or Natural Sounds in the Auditory Cortex. Frontiers in Neural Circuits. 13: 82. PMID 32047424 DOI: 10.3389/fncir.2019.00082 |
0.36 |
|
| 2019 |
Gjorgjieva J, Meister M, Sompolinsky H. Functional diversity among sensory neurons from efficient coding principles. Plos Computational Biology. 15: e1007476. PMID 31725714 DOI: 10.1371/Journal.Pcbi.1007476 |
0.538 |
|
| 2018 |
Landau ID, Sompolinsky H. Coherent chaos in a recurrent neural network with structured connectivity. Plos Computational Biology. 14: e1006309. PMID 30543634 DOI: 10.1371/journal.pcbi.1006309 |
0.388 |
|
| 2018 |
Chen X, Mu Y, Hu Y, Kuan AT, Nikitchenko M, Randlett O, Chen AB, Gavornik JP, Sompolinsky H, Engert F, Ahrens MB. Brain-wide Organization of Neuronal Activity and Convergent Sensorimotor Transformations in Larval Zebrafish. Neuron. 100: 876-890.e5. PMID 30473013 DOI: 10.1016/J.Neuron.2018.09.042 |
0.339 |
|
| 2018 |
Chung S, Cohen U, Sompolinsky H, Lee DD. Learning Data Manifolds with a Cutting Plane Method. Neural Computation. 1-23. PMID 30148702 DOI: 10.1162/Neco_A_01119 |
0.753 |
|
| 2018 |
Chung S, Lee DD, Sompolinsky H. Classification and Geometry of General Perceptual Manifolds Physical Review X. 8. DOI: 10.1103/PhysRevX.8.031003 |
0.797 |
|
| 2017 |
Rubin R, Abbott LF, Sompolinsky H. 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. PMID 29042519 DOI: 10.1073/pnas.1705841114 |
0.625 |
|
| 2017 |
Litwin-Kumar A, Harris KD, Axel R, Sompolinsky H, Abbott LF. Optimal Degrees of Synaptic Connectivity. Neuron. PMID 28215558 DOI: 10.1016/J.Neuron.2017.01.030 |
0.589 |
|
| 2016 |
Landau ID, Egger R, Dercksen VJ, Oberlaender M, Sompolinsky H. The Impact of Structural Heterogeneity on Excitation-Inhibition Balance in Cortical Networks. Neuron. PMID 27866797 DOI: 10.1016/J.Neuron.2016.10.027 |
0.346 |
|
| 2016 |
Naumann EA, Fitzgerald JE, Dunn TW, Rihel J, Sompolinsky H, Engert F. From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response. Cell. 167: 947-960.e20. PMID 27814522 DOI: 10.1016/J.Cell.2016.10.019 |
0.311 |
|
| 2016 |
Sharpee TO, Destexhe A, Kawato M, Sekulić V, Skinner FK, Wójcik DK, Chintaluri C, Cserpán D, Somogyvári Z, Kim JK, Kilpatrick ZP, Bennett MR, Josić K, Elices I, Arroyo D, ... ... Sompolinsky H, et al. 25th Annual Computational Neuroscience Meeting: CNS-2016 Bmc Neuroscience. 17: 54. PMID 27534393 DOI: 10.1186/S12868-016-0283-6 |
0.733 |
|
| 2016 |
Chung S, Lee DD, Sompolinsky H. Linear readout of object manifolds. Physical Review. E. 93: 060301. PMID 27415193 DOI: 10.1103/PhysRevE.93.060301 |
0.795 |
|
| 2015 |
Kadmon J, Sompolinsky H. Transition to chaos in random neuronal networks Physical Review X. 5. DOI: 10.1103/PhysRevX.5.041030 |
0.423 |
|
| 2014 |
Stern M, Sompolinsky H, Abbott LF. Dynamics of random neural networks with bistable units. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 90: 062710. PMID 25615132 DOI: 10.1103/PhysRevE.90.062710 |
0.602 |
|
| 2014 |
Gjorgjieva J, Sompolinsky H, Meister M. Benefits of pathway splitting in sensory coding. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 34: 12127-44. PMID 25186757 DOI: 10.1523/Jneurosci.1032-14.2014 |
0.546 |
|
| 2014 |
Babadi B, Sompolinsky H. Sparseness and expansion in sensory representations. Neuron. 83: 1213-26. PMID 25155954 DOI: 10.1016/j.neuron.2014.07.035 |
0.382 |
|
| 2014 |
Memmesheimer RM, Rubin R, Olveczky BP, Sompolinsky H. Learning precisely timed spikes. Neuron. 82: 925-38. PMID 24768299 DOI: 10.1016/j.neuron.2014.03.026 |
0.424 |
|
| 2014 |
Sompolinsky H. Computational neuroscience: beyond the local circuit. Current Opinion in Neurobiology. 25: xiii-xviii. PMID 24602868 DOI: 10.1016/j.conb.2014.02.002 |
0.405 |
|
| 2014 |
Pehlevan C, Sompolinsky H. Selectivity and sparseness in randomly connected balanced networks. Plos One. 9: e89992. PMID 24587172 DOI: 10.1371/Journal.Pone.0089992 |
0.743 |
|
| 2013 |
Gütig R, Gollisch T, Sompolinsky H, Meister M. Computing complex visual features with retinal spike times. Plos One. 8: e53063. PMID 23301021 DOI: 10.1371/journal.pone.0053063 |
0.79 |
|
| 2012 |
Ganguli S, Sompolinsky H. Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis. Annual Review of Neuroscience. 35: 485-508. PMID 22483042 DOI: 10.1146/Annurev-Neuro-062111-150410 |
0.337 |
|
| 2012 |
Rokni U, Sompolinsky H. How the brain generates movement. Neural Computation. 24: 289-331. PMID 22023199 DOI: 10.1162/NECO_a_00223 |
0.334 |
|
| 2011 |
Abbott LF, Rajan K, Sompolinsky H. Interactions between Intrinsic and Stimulus-Evoked Activity in Recurrent Neural Networks The Dynamic Brain: An Exploration of Neuronal Variability and Its Functional Significance. DOI: 10.1093/acprof:oso/9780195393798.003.0004 |
0.525 |
|
| 2011 |
Burak Y, Rokni U, Meister M, Sompolinsky H. Reply to Wehrhahn: Experimental requirements for testing the role of peripheral cues in dynamic image stabilization Proceedings of the National Academy of Sciences of the United States of America. 108: E36. DOI: 10.1073/Pnas.1100198108 |
0.687 |
|
| 2010 |
Rubin R, Monasson R, Sompolinsky H. Theory of spike timing-based neural classifiers. Physical Review Letters. 105: 218102. PMID 21231357 DOI: 10.1103/Physrevlett.105.218102 |
0.375 |
|
| 2010 |
Burak Y, Rokni U, Meister M, Sompolinsky H. Bayesian model of dynamic image stabilization in the visual system. Proceedings of the National Academy of Sciences of the United States of America. 107: 19525-30. PMID 20937893 DOI: 10.1073/Pnas.1006076107 |
0.666 |
|
| 2010 |
Rajan K, Abbott LF, Sompolinsky H. Stimulus-dependent suppression of chaos in recurrent neural networks. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 82: 011903. PMID 20866644 DOI: 10.1103/PhysRevE.82.011903 |
0.608 |
|
| 2010 |
Rajan K, Abbott LF, Sompolinsky H. Stimulus-dependent suppression of intrinsic variability in recurrent neural networks Bmc Neuroscience. 11. DOI: 10.1186/1471-2202-11-S1-O17 |
0.424 |
|
| 2010 |
Rajan K, Abbott LF, Sompolinsky H. Inferring stimulus selectivity from the spatial structure of neural network dynamics Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010. |
0.527 |
|
| 2009 |
Gütig R, Sompolinsky H. Time-warp-invariant neuronal processing. Plos Biology. 7: e1000141. PMID 19582146 DOI: 10.1371/journal.pbio.1000141 |
0.795 |
|
| 2009 |
Burak Y, Lewallen S, Sompolinsky H. Stimulus-dependent correlations in threshold-crossing spiking neurons. Neural Computation. 21: 2269-308. PMID 19409055 DOI: 10.1162/Neco.2009.07-08-830 |
0.666 |
|
| 2008 |
Ganguli S, Huh D, Sompolinsky H. Memory traces in dynamical systems. Proceedings of the National Academy of Sciences of the United States of America. 105: 18970-5. PMID 19020074 DOI: 10.1073/Pnas.0804451105 |
0.602 |
|
| 2007 |
Pitkow X, Sompolinsky H, Meister M. A neural computation for visual acuity in the presence of eye movements. Plos Biology. 5: e331. PMID 18162043 DOI: 10.1371/Journal.Pbio.0050331 |
0.733 |
|
| 2006 |
Shamir M, Sompolinsky H. Implications of neuronal diversity on population coding. Neural Computation. 18: 1951-86. PMID 16771659 DOI: 10.1162/neco.2006.18.8.1951 |
0.394 |
|
| 2006 |
Gütig R, Sompolinsky H. The tempotron: a neuron that learns spike timing-based decisions. Nature Neuroscience. 9: 420-8. PMID 16474393 DOI: 10.1038/nn1643 |
0.792 |
|
| 2006 |
Loewenstein Y, Mahon S, Chadderton P, Kitamura K, Sompolinsky H, Yarom Y, Häusser M. Loewenstein et al. reply [2] Nature Neuroscience. 9: 461. DOI: 10.1038/nn0406-461 |
0.732 |
|
| 2005 |
Loewenstein Y, Mahon S, Chadderton P, Kitamura K, Sompolinsky H, Yarom Y, Häusser M. Bistability of cerebellar Purkinje cells modulated by sensory stimulation. Nature Neuroscience. 8: 202-11. PMID 15665875 DOI: 10.1038/nn1393 |
0.788 |
|
| 2005 |
van Vreeswijk C, Sompolinsky H. Course 9 Irregular activity in large networks of neurons Les Houches Summer School Proceedings. 80: 341-406. DOI: 10.1016/S0924-8099(05)80015-0 |
0.714 |
|
| 2004 |
Goldberg JA, Rokni U, Sompolinsky H. Patterns of ongoing activity and the functional architecture of the primary visual cortex. Neuron. 42: 489-500. PMID 15134644 DOI: 10.1016/S0896-6273(04)00197-7 |
0.595 |
|
| 2004 |
Shamir M, Sompolinsky H. Nonlinear population codes. Neural Computation. 16: 1105-36. PMID 15130244 DOI: 10.1162/089976604773717559 |
0.371 |
|
| 2004 |
White OL, Lee DD, Sompolinsky H. Short-term memory in orthogonal neural networks. Physical Review Letters. 92: 148102. PMID 15089576 DOI: 10.1103/Physrevlett.92.148102 |
0.494 |
|
| 2004 |
Kang K, Shapley RM, Sompolinsky H. Information tuning of populations of neurons in primary visual cortex. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 24: 3726-35. PMID 15084652 DOI: 10.1523/JNEUROSCI.4272-03.2004 |
0.373 |
|
| 2003 |
Shriki O, Hansel D, Sompolinsky H. Rate models for conductance-based cortical neuronal networks. Neural Computation. 15: 1809-41. PMID 14511514 DOI: 10.1162/08997660360675053 |
0.785 |
|
| 2003 |
Loewenstein Y, Sompolinsky H. Temporal integration by calcium dynamics in a model neuron. Nature Neuroscience. 6: 961-7. PMID 12937421 DOI: 10.1038/nn1109 |
0.764 |
|
| 2003 |
Gütig R, Aharonov R, Rotter S, Sompolinsky H. Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 23: 3697-714. PMID 12736341 DOI: 10.1523/Jneurosci.23-09-03697.2003 |
0.33 |
|
| 2003 |
Litvak V, Sompolinsky H, Segev I, Abeles M. On the transmission of rate code in long feedforward networks with excitatory-inhibitory balance. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 23: 3006-15. PMID 12684488 DOI: 10.1523/Jneurosci.23-07-03006.2003 |
0.586 |
|
| 2003 |
Kang K, Shelley M, Sompolinsky H. Mexican hats and pinwheels in visual cortex. Proceedings of the National Academy of Sciences of the United States of America. 100: 2848-53. PMID 12601163 DOI: 10.1073/Pnas.0138051100 |
0.349 |
|
| 2002 |
Loewenstein Y, Sompolinsky H. Oscillations by symmetry breaking in homogeneous networks with electrical coupling. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 65: 051926. PMID 12059612 DOI: 10.1103/PhysRevE.65.051926 |
0.76 |
|
| 2001 |
Sompolinsky H, Yoon H, Kang K, Shamir M. Population coding in neuronal systems with correlated noise. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 64: 051904. PMID 11735965 DOI: 10.1103/Physreve.64.051904 |
0.347 |
|
| 2001 |
Loewenstein Y, Yarom Y, Sompolinsky H. The generation of oscillations in networks of electrically coupled cells. Proceedings of the National Academy of Sciences of the United States of America. 98: 8095-100. PMID 11427705 DOI: 10.1073/pnas.131116898 |
0.771 |
|
| 2001 |
Rubin J, Lee DD, Sompolinsky H. Equilibrium properties of temporally asymmetric Hebbian plasticity. Physical Review Letters. 86: 364-7. PMID 11177832 DOI: 10.1103/Physrevlett.86.364 |
0.47 |
|
| 2001 |
Shriki O, Sompolinsky H, Lee DD. An information maximization approach to overcomplete and recurrent representations Advances in Neural Information Processing Systems. |
0.655 |
|
| 1999 |
Dietrich R, Opper M, Sompolinsky H. Statistical mechanics of support vector networks Physical Review Letters. 82: 2975-2978. DOI: 10.1103/Physrevlett.82.2975 |
0.345 |
|
| 1998 |
van Vreeswijk C, Sompolinsky H. Chaotic balanced state in a model of cortical circuits. Neural Computation. 10: 1321-71. PMID 9698348 DOI: 10.1162/089976698300017214 |
0.745 |
|
| 1997 |
Ben-Yishai R, Hansel D, Sompolinsky H. Traveling waves and the processing of weakly tuned inputs in a cortical network module. Journal of Computational Neuroscience. 4: 57-77. PMID 9046452 DOI: 10.1023/A:1008816611284 |
0.599 |
|
| 1996 |
van Vreeswijk C, Sompolinsky H. Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science (New York, N.Y.). 274: 1724-6. PMID 8939866 DOI: 10.1126/science.274.5293.1724 |
0.756 |
|
| 1996 |
Hansel D, Sompolinsky H. Chaos and synchrony in a model of a hypercolumn in visual cortex. Journal of Computational Neuroscience. 3: 7-34. PMID 8717487 DOI: 10.1007/Bf00158335 |
0.644 |
|
| 1996 |
Mato G, Sompolinsky H. Neural network models of perceptual learning of angle discrimination. Neural Computation. 8: 270-99. PMID 8581884 DOI: 10.1162/Neco.1996.8.2.270 |
0.377 |
|
| 1995 |
Barkai N, Seung HS, Sompolinsky H. Local and global convergence of on-line learning. Physical Review Letters. 75: 1415-1418. PMID 10060287 DOI: 10.1103/PhysRevLett.75.1415 |
0.636 |
|
| 1995 |
Ben-Yishai R, Bar-Or RL, Sompolinsky H. Theory of orientation tuning in visual cortex. Proceedings of the National Academy of Sciences of the United States of America. 92: 3844-8. PMID 7731993 DOI: 10.1073/Pnas.92.9.3844 |
0.374 |
|
| 1994 |
Ginzburg I, Sompolinsky H. Theory of correlations in stochastic neural networks. Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics. 50: 3171-3191. PMID 9962363 DOI: 10.1103/PhysRevE.50.3171 |
0.342 |
|
| 1994 |
Sompolinsky H, Tsodyks M. Segmentation by a Network of Oscillators with Stored Memories Neural Computation. 6: 642-657. DOI: 10.1162/neco.1994.6.4.642 |
0.619 |
|
| 1993 |
Tsodyks M, Mitkov I, Sompolinsky H. Pattern of synchrony in inhomogeneous networks of oscillators with pulse interactions. Physical Review Letters. 71: 1280-1283. PMID 10055496 DOI: 10.1103/PhysRevLett.71.1280 |
0.592 |
|
| 1993 |
Hansel D, Sompolinsky H. Solvable model of spatiotemporal chaos. Physical Review Letters. 71: 2710-2713. PMID 10054756 DOI: 10.1103/Physrevlett.71.2710 |
0.528 |
|
| 1993 |
Barkai N, Seung HS, Sompolinsky H. Scaling laws in learning of classification tasks. Physical Review Letters. 70: 3167-3170. PMID 10053792 DOI: 10.1103/PhysRevLett.70.3167 |
0.617 |
|
| 1993 |
Seung HS, Sompolinsky H. Simple models for reading neuronal population codes. Proceedings of the National Academy of Sciences of the United States of America. 90: 10749-53. PMID 8248166 DOI: 10.1073/Pnas.90.22.10749 |
0.72 |
|
| 1993 |
Grannan ER, Kleinfeld D, Sompolinsky H. Stimulus-Dependent Synchronization of Neuronal Assemblies Neural Computation. 5: 550-569. DOI: 10.1162/neco.1993.5.4.550 |
0.6 |
|
| 1992 |
Hansel D, Sompolinsky H. Synchronization and computation in a chaotic neural network. Physical Review Letters. 68: 718-721. PMID 10045972 DOI: 10.1103/Physrevlett.68.718 |
0.623 |
|
| 1992 |
Aranson I, Golomb D, Sompolinsky H. Spatial coherence and temporal chaos in macroscopic systems with asymmetrical couplings. Physical Review Letters. 68: 3495-3498. PMID 10045719 DOI: 10.1103/PhysRevLett.68.3495 |
0.591 |
|
| 1992 |
Seung HS, Sompolinsky H, Tishby N. Statistical mechanics of learning from examples. Physical Review. A. 45: 6056-6091. PMID 9907706 DOI: 10.1103/PhysRevA.45.6056 |
0.766 |
|
| 1992 |
Barkai E, Hansel D, Sompolinsky H. Broken symmetries in multilayered perceptrons. Physical Review. A. 45: 4146-4161. PMID 9907466 DOI: 10.1103/PhysRevA.45.4146 |
0.441 |
|
| 1992 |
Golomb D, Hansel D, Shraiman B, Sompolinsky H. Clustering in globally coupled phase oscillators. Physical Review. A. 45: 3516-3530. PMID 9907399 DOI: 10.1103/Physreva.45.3516 |
0.752 |
|
| 1992 |
Sompolinsky H, Tsodyks M. PROCESSING OF SENSORY INFORMATION BY A NETWORK OF OSCILLATORS WITH MEMORY International Journal of Neural Systems. 3: 51-56. DOI: 10.1142/S0129065792000371 |
0.624 |
|
| 1992 |
Seung HS, Opper M, Sompolinsky H. Query by committee Proceedings of the Fifth Annual Acm Workshop On Computational Learning Theory. 287-294. |
0.563 |
|
| 1991 |
Sompolinsky H, Golomb D, Kleinfeld D. Cooperative dynamics in visual processing. Physical Review. A. 43: 6990-7011. PMID 9905051 DOI: 10.1103/PhysRevA.43.6990 |
0.663 |
|
| 1990 |
Sompolinsky H, Tishby N, Seung HS. Learning from examples in large neural networks. Physical Review Letters. 65: 1683-1686. PMID 10042332 DOI: 10.1103/PhysRevLett.65.1683 |
0.747 |
|
| 1990 |
Golomb D, Rubin N, Sompolinsky H. Willshaw model: Associative memory with sparse coding and low firing rates. Physical Review. A. 41: 1843-1854. PMID 9903293 DOI: 10.1103/PhysRevA.41.1843 |
0.696 |
|
| 1990 |
Barkai E, Kanter I, Sompolinsky H. Properties of sparsely connected excitatory neural networks. Physical Review. A. 41: 590-597. PMID 9903143 DOI: 10.1103/Physreva.41.590 |
0.417 |
|
| 1990 |
Sompolinsky H, Golomb D, Kleinfeld D. Global processing of visual stimuli in a neural network of coupled oscillators. Proceedings of the National Academy of Sciences of the United States of America. 87: 7200-4. PMID 2402502 DOI: 10.1073/Pnas.87.18.7200 |
0.743 |
|
| 1990 |
Sompolinsky H, Tishby N. Learning in a two-layer neural network of edge detectors Epl. 13: 567-572. DOI: 10.1209/0295-5075/13/6/016 |
0.629 |
|
| 1989 |
Rubin N, Sompolinsky H. Neural networks with low local firing rates Epl. 10: 465-470. DOI: 10.1209/0295-5075/10/5/013 |
0.622 |
|
| 1988 |
Sompolinsky H, Crisanti A, Sommers HJ. Chaos in random neural networks. Physical Review Letters. 61: 259-262. PMID 10039285 DOI: 10.1103/PhysRevLett.61.259 |
0.348 |
|
| 1988 |
Crisanti A, Sompolinsky H. Dynamics of spin systems with randomly asymmetric bonds: Ising spins and Glauber dynamics. Physical Review. A. 37: 4865-4874. PMID 9899634 DOI: 10.1103/PhysRevA.37.4865 |
0.304 |
|
| 1988 |
Kleinfeld D, Sompolinsky H. Associative neural network model for the generation of temporal patterns. Theory and application to central pattern generators. Biophysical Journal. 54: 1039-51. PMID 3233265 DOI: 10.1016/S0006-3495(88)83041-8 |
0.617 |
|
| 1988 |
Sompolinsky H. Statistical Mechanics of Neural Networks Physics Today. 41: 70-80. DOI: 10.1063/1.881142 |
0.409 |
|
| 1987 |
Kotliar G, Sompolinsky H, Zippelius A. Rotational symmetry breaking in Heisenberg spin glasses: A microscopic approach. Physical Review. B, Condensed Matter. 35: 311-328. PMID 9940601 DOI: 10.1103/PhysRevB.35.311 |
0.437 |
|
| 1987 |
Crisanti A, Sompolinsky H. Dynamics of spin systems with randomly asymmetric bonds: Langevin dynamics and a spherical model. Physical Review. A. 36: 4922-4939. PMID 9898751 DOI: 10.1103/PhysRevA.36.4922 |
0.356 |
|
| 1987 |
Amit DJ, Gutfreund H, Sompolinsky H. Information storage in neural networks with low levels of activity. Physical Review. A. 35: 2293-2303. PMID 9898407 DOI: 10.1103/PhysRevA.35.2293 |
0.4 |
|
| 1987 |
Amit DJ, Gutfreund H, Sompolinsky H. Statistical mechanics of neural networks near saturation Annals of Physics. 173: 30-67. DOI: 10.1016/0003-4916(87)90092-3 |
0.388 |
|
| 1986 |
Sompolinsky H, Kanter I. Temporal association in asymmetric neural networks. Physical Review Letters. 57: 2861-2864. PMID 10033885 DOI: 10.1103/PhysRevLett.57.2861 |
0.407 |
|
| 1986 |
Sompolinsky H. Neural networks with nonlinear synapses and a static noise. Physical Review. A. 34: 2571-2574. PMID 9897569 DOI: 10.1103/PhysRevA.34.2571 |
0.344 |
|
| 1985 |
Amit DJ, Gutfreund H, Sompolinsky H. Storing infinite numbers of patterns in a spin-glass model of neural networks. Physical Review Letters. 55: 1530-1533. PMID 10031847 DOI: 10.1103/PhysRevLett.55.1530 |
0.313 |
|
| 1985 |
Fisher DS, Sompolinsky H. Scaling in spin-glasses. Physical Review Letters. 54: 1063-1066. PMID 10030919 DOI: 10.1103/PhysRevLett.54.1063 |
0.466 |
|
| 1985 |
Amit DJ, Gutfreund H, Sompolinsky H. Spin-glass models of neural networks. Physical Review. A. 32: 1007-1018. PMID 9896156 DOI: 10.1103/PhysRevA.32.1007 |
0.338 |
|
| 1984 |
Sompolinsky H, Kotliar G, Zippelius A. Exchange stiffness and macroscopic anisotropy in Heisenberg spin-glasses Physical Review Letters. 52: 392-395. DOI: 10.1103/Physrevlett.52.392 |
0.514 |
|
| 1983 |
Sompolinsky H, Zippelius A. Fluctuations in short-range spin-glasses Physical Review Letters. 50: 1297-1300. DOI: 10.1103/PhysRevLett.50.1297 |
0.501 |
|
| 1983 |
John S, Sompolinsky H, Stephen MJ. Localization in a disordered elastic medium near two dimensions Physical Review B. 27: 5592-5603. DOI: 10.1103/Physrevb.27.5592 |
0.448 |
|
| 1983 |
Dasgupta C, Sompolinsky H. Equivalence of statistical-mechanical and dynamic descriptions of the infinite-range Ising spin-glass Physical Review B. 27: 4511-4514. DOI: 10.1103/Physrevb.27.4511 |
0.54 |
|
| 1982 |
Sompolinsky H, Zippelius A. Relaxational dynamics of the Edwards-Anderson model and the mean-field theory of spin-glasses Physical Review B. 25: 6860-6875. DOI: 10.1103/PhysRevB.25.6860 |
0.524 |
|
| 1982 |
Henley CL, Sompolinsky H, Halperin BI. Spin-resonance frequencies in spin-glasses with random anisotropies Physical Review B. 25: 5849-5855. DOI: 10.1103/Physrevb.25.5849 |
0.637 |
|
| 1982 |
Sompolinsky H, Zippelius A. Relaxational dynamics of the infinite-ranged spin glass with n-component spins Journal of Physics C: Solid State Physics. 15: L1059-L1064. DOI: 10.1088/0022-3719/15/30/003 |
0.444 |
|
| 1981 |
Sompolinsky H, Zippelius A. Dynamic Theory of the Spin-Glass Phase Physical Review Letters. 47: 359-362. DOI: 10.1103/PhysRevLett.47.359 |
0.517 |
|
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