Year |
Citation |
Score |
2024 |
Steinfeld R, Tacão-Monteiro A, Renart A. Differential representation of sensory information and behavioral choice across layers of the mouse auditory cortex. Current Biology : Cb. 34: 2200-2211.e6. PMID 38733991 DOI: 10.1016/j.cub.2024.04.040 |
0.382 |
|
2023 |
Reato D, Steinfeld R, Tacão-Monteiro A, Renart A. Response outcome gates the effect of spontaneous cortical state fluctuations on perceptual decisions. Elife. 12. PMID 37195029 DOI: 10.7554/eLife.81774 |
0.787 |
|
2021 |
Steinmetz NA, Aydin C, Lebedeva A, Okun M, Pachitariu M, Bauza M, Beau M, Bhagat J, Böhm C, Broux M, Chen S, Colonell J, Gardner RJ, Karsh B, Kloosterman F, ... ... Renart A, et al. Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings. Science (New York, N.Y.). 372. PMID 33859006 DOI: 10.1126/science.abf4588 |
0.751 |
|
2020 |
Cazettes F, Reato D, Morais JP, Renart A, Mainen ZF. Phasic Activation of Dorsal Raphe Serotonergic Neurons Increases Pupil Size. Current Biology : Cb. PMID 33186549 DOI: 10.1016/j.cub.2020.09.090 |
0.741 |
|
2019 |
Pardo-Vazquez JL, Castiñeiras-de Saa JR, Valente M, Damião I, Costa T, Vicente MI, Mendonça AG, Mainen ZF, Renart A. The mechanistic foundation of Weber's law. Nature Neuroscience. PMID 31406366 DOI: 10.1038/S41593-019-0439-7 |
0.366 |
|
2019 |
Kobak D, Pardo-Vazquez JL, Valente M, Machens CK, Renart A. State-dependent geometry of population activity in rat auditory cortex. Elife. 8. PMID 30969167 DOI: 10.7554/Elife.44526 |
0.427 |
|
2019 |
Kobak D, Pardo-Vazquez JL, Valente M, Machens CK, Renart A. Author response: State-dependent geometry of population activity in rat auditory cortex Elife. DOI: 10.7554/Elife.44526.022 |
0.426 |
|
2015 |
Wimmer K, Compte A, Roxin A, Peixoto D, Renart A, de la Rocha J. Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT. Nature Communications. 6: 6177. PMID 25649611 DOI: 10.1038/Ncomms7177 |
0.769 |
|
2014 |
Renart A. Bringing the dynamics of movement under control. Neuron. 82: 1193-5. PMID 24945762 DOI: 10.1016/J.Neuron.2014.06.002 |
0.362 |
|
2013 |
Renart A. Recurrent networks learn to tell time. Nature Neuroscience. 16: 772-4. PMID 23799465 DOI: 10.1038/Nn.3441 |
0.346 |
|
2012 |
Renart A, van Rossum MC. Transmission of population-coded information. Neural Computation. 24: 391-407. PMID 22023200 DOI: 10.1162/Neco_A_00227 |
0.448 |
|
2011 |
Harris KD, Bartho P, Chadderton P, Curto C, de la Rocha J, Hollender L, Itskov V, Luczak A, Marguet SL, Renart A, Sakata S. How do neurons work together? Lessons from auditory cortex. Hearing Research. 271: 37-53. PMID 20603208 DOI: 10.1016/J.Heares.2010.06.006 |
0.647 |
|
2011 |
Manrique J, Renart A, de la Rocha J, Parga N. Scaling of temporal correlations in densely connected networks of LIF neurons Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P249 |
0.713 |
|
2010 |
Renart A, de la Rocha J, Bartho P, Hollender L, Parga N, Reyes A, Harris KD. The asynchronous state in cortical circuits. Science (New York, N.Y.). 327: 587-90. PMID 20110507 DOI: 10.1126/Science.1179850 |
0.748 |
|
2008 |
Moreno-Bote R, Renart A, Parga N. Theory of input spike auto- and cross-correlations and their effect on the response of spiking neurons. Neural Computation. 20: 1651-705. PMID 18254697 DOI: 10.1162/Neco.2008.03-07-497 |
0.81 |
|
2007 |
Renart A, Moreno-Bote R, Wang XJ, Parga N. Mean-driven and fluctuation-driven persistent activity in recurrent networks. Neural Computation. 19: 1-46. PMID 17134316 DOI: 10.1162/Neco.2007.19.1.1 |
0.822 |
|
2004 |
van Rossum MCW, Renart A. Computation with populations codes in layered networks of integrate-and-fire neurons Neurocomputing. 58: 265-270. DOI: 10.1016/J.Neucom.2004.01.054 |
0.406 |
|
2003 |
Renart A, Song P, Wang XJ. Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks. Neuron. 38: 473-85. PMID 12741993 DOI: 10.1016/S0896-6273(03)00255-1 |
0.683 |
|
2002 |
Moreno R, de la Rocha J, Renart A, Parga N. Response of spiking neurons to correlated inputs. Physical Review Letters. 89: 288101. PMID 12513181 DOI: 10.1103/Physrevlett.89.288101 |
0.705 |
|
2002 |
Moreno R, De La Rocha J, Renart A, Parga N. Response of spiking neurons to correlated inputs Physical Review Letters. 89: 288101/1-288101/4. |
0.778 |
|
2001 |
Renart A, Moreno R, De la Rocha J, Parga N, Rolls ET. A model of the IT-PF network in object working memory which includes balanced persistent activity and tuned inhibition Neurocomputing. 38: 1525-1531. DOI: 10.1016/S0925-2312(01)00548-3 |
0.769 |
|
2000 |
Elliffe MC, Rolls ET, Parga N, Renart A. A recurrent model of transformation invariance by association. Neural Networks : the Official Journal of the International Neural Network Society. 13: 225-37. PMID 10935762 DOI: 10.1016/S0893-6080(99)00096-9 |
0.603 |
|
2000 |
Renart A, Parga N, Rolls ET. A recurrent model of the interaction between Prefrontal and Inferotemporal cortex in delay tasks Advances in Neural Information Processing Systems. 171-177. |
0.581 |
|
1999 |
Renart A, Parga N, Rolls ET. Associative memory properties of multiple cortical modules. Network (Bristol, England). 10: 237-55. PMID 10496475 DOI: 10.1088/0954-898X_10_3_303 |
0.72 |
|
1999 |
Renart A, Parga N, Rolls ET. Backward projections in the cerebral cortex: implications for memory storage. Neural Computation. 11: 1349-88. PMID 10423499 DOI: 10.1162/089976699300016278 |
0.664 |
|
1999 |
Renart A, Parga N, Rolls ET. Connected cortical recurrent networks Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1606: 163-170. DOI: 10.1007/Bfb0098170 |
0.671 |
|
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