| Year |
Citation |
Score |
| 2023 |
Wybo WAM, Tsai MC, Tran VAK, Illing B, Jordan J, Morrison A, Senn W. NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways. Proceedings of the National Academy of Sciences of the United States of America. 120: e2300558120. PMID 37523562 DOI: 10.1073/pnas.2300558120 |
0.358 |
|
| 2022 |
Kreutzer E, Senn W, Petrovici MA. Natural-gradient learning for spiking neurons. Elife. 11. PMID 35467527 DOI: 10.7554/eLife.66526 |
0.388 |
|
| 2021 |
Jordan J, Schmidt M, Senn W, Petrovici MA. Evolving interpretable plasticity for spiking networks. Elife. 10. PMID 34709176 DOI: 10.7554/eLife.66273 |
0.328 |
|
| 2021 |
Wybo WA, Jordan J, Ellenberger B, Marti Mengual U, Nevian T, Senn W. Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses. Elife. 10. PMID 33494860 DOI: 10.7554/eLife.60936 |
0.301 |
|
| 2020 |
Marti Mengual U, Wybo WAM, Spierenburg LJE, Santello M, Senn W, Nevian T. Efficient low-pass dendro-somatic coupling in the apical dendrite of layer 5 pyramidal neurons in the anterior cingulate cortex. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. PMID 33046549 DOI: 10.1523/JNEUROSCI.3028-19.2020 |
0.378 |
|
| 2019 |
Richards BA, Lillicrap TP, Beaudoin P, Bengio Y, Bogacz R, Christensen A, Clopath C, Costa RP, de Berker A, Ganguli S, Gillon CJ, Hafner D, Kepecs A, Kriegeskorte N, Latham P, ... ... Senn W, et al. A deep learning framework for neuroscience. Nature Neuroscience. 22: 1761-1770. PMID 31659335 DOI: 10.1038/S41593-019-0520-2 |
0.728 |
|
| 2019 |
Dold D, Bytschok I, Kungl AF, Baumbach A, Breitwieser O, Senn W, Schemmel J, Meier K, Petrovici MA. Stochasticity from function - Why the Bayesian brain may need no noise. Neural Networks : the Official Journal of the International Neural Network Society. 119: 200-213. PMID 31450073 DOI: 10.1016/J.Neunet.2019.08.002 |
0.472 |
|
| 2018 |
de Andres-Bragado L, Mazza C, Senn W, Sprecher SG. Statistical modelling of navigational decisions based on intensity versus directionality in Drosophila larval phototaxis. Scientific Reports. 8: 11272. PMID 30050066 DOI: 10.1038/S41598-018-29533-0 |
0.329 |
|
| 2018 |
Leng L, Martel R, Breitwieser O, Bytschok I, Senn W, Schemmel J, Meier K, Petrovici MA. Spiking neurons with short-term synaptic plasticity form superior generative networks. Scientific Reports. 8: 10651. PMID 30006554 DOI: 10.1038/S41598-018-28999-2 |
0.453 |
|
| 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, ... ... Senn W, et al. 25th Annual Computational Neuroscience Meeting: CNS-2016 Bmc Neuroscience. 17: 54. PMID 27534393 DOI: 10.1186/S12868-016-0283-6 |
0.735 |
|
| 2016 |
Brea J, Gaál AT, Urbanczik R, Senn W. Prospective Coding by Spiking Neurons. Plos Computational Biology. 12: e1005003. PMID 27341100 DOI: 10.1371/Journal.Pcbi.1005003 |
0.83 |
|
| 2016 |
Schiess M, Urbanczik R, Senn W. Somato-dendritic Synaptic Plasticity and Error-backpropagation in Active Dendrites. Plos Computational Biology. 12: e1004638. PMID 26841235 DOI: 10.1371/Journal.Pcbi.1004638 |
0.721 |
|
| 2015 |
Vladimirskiy B, Urbanczik R, Senn W. Hierarchical Novelty-Familiarity Representation in the Visual System by Modular Predictive Coding. Plos One. 10: e0144636. PMID 26670700 DOI: 10.1371/Journal.Pone.0144636 |
0.642 |
|
| 2015 |
Senn W, Sacramento J. Backward reasoning the formation rules. Nature Neuroscience. 18: 1705-1706. PMID 26605880 DOI: 10.1038/nn.4172 |
0.476 |
|
| 2015 |
Khajeh-Alijani A, Urbanczik R, Senn W. Scale-Free Navigational Planning by Neuronal Traveling Waves. Plos One. 10: e0127269. PMID 26158660 DOI: 10.1371/Journal.Pone.0127269 |
0.658 |
|
| 2015 |
Clarke AM, Friedrich J, Tartaglia EM, Marchesotti S, Senn W, Herzog MH. Human and machine learning in non-Markovian decision making. Plos One. 10: e0123105. PMID 25898139 DOI: 10.1371/Journal.Pone.0123105 |
0.414 |
|
| 2015 |
Senn W, Brea J. Neurons that Remember How We Got There. Neuron. 85: 664-6. PMID 25695266 DOI: 10.1016/j.neuron.2015.01.029 |
0.808 |
|
| 2014 |
Lüdge T, Urbanczik R, Senn W. Modulation of orientation-selective neurons by motion: when additive, when multiplicative? Frontiers in Computational Neuroscience. 8: 67. PMID 24999328 DOI: 10.3389/Fncom.2014.00067 |
0.667 |
|
| 2014 |
Brea J, Urbanczik R, Senn W. A normative theory of forgetting: lessons from the fruit fly. Plos Computational Biology. 10: e1003640. PMID 24901935 DOI: 10.1371/Journal.Pcbi.1003640 |
0.779 |
|
| 2014 |
Friedrich J, Urbanczik R, Senn W. Code-specific learning rules improve action selection by populations of spiking neurons. International Journal of Neural Systems. 24: 1450002. PMID 24875790 DOI: 10.1142/S0129065714500026 |
0.688 |
|
| 2014 |
Blom SM, Pfister JP, Santello M, Senn W, Nevian T. Nerve injury-induced neuropathic pain causes disinhibition of the anterior cingulate cortex. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 34: 5754-64. PMID 24760836 DOI: 10.1523/Jneurosci.3667-13.2014 |
0.603 |
|
| 2014 |
Urbanczik R, Senn W. Learning by the dendritic prediction of somatic spiking. Neuron. 81: 521-8. PMID 24507189 DOI: 10.1016/J.Neuron.2013.11.030 |
0.712 |
|
| 2013 |
Brea J, Senn W, Pfister JP. Matching recall and storage in sequence learning with spiking neural networks. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 33: 9565-75. PMID 23739954 DOI: 10.1523/JNEUROSCI.4098-12.2013 |
0.833 |
|
| 2012 |
Friedrich J, Senn W. Spike-based decision learning of Nash equilibria in two-player games. Plos Computational Biology. 8: e1002691. PMID 23028289 DOI: 10.1371/journal.pcbi.1002691 |
0.422 |
|
| 2012 |
Schiess M, Urbanczik R, Senn W. Gradient estimation in dendritic reinforcement learning. Journal of Mathematical Neuroscience. 2: 2. PMID 22657827 DOI: 10.1186/2190-8567-2-2 |
0.727 |
|
| 2011 |
Friedrich J, Urbanczik R, Senn W. Spatio-temporal credit assignment in neuronal population learning. Plos Computational Biology. 7: e1002092. PMID 21738460 DOI: 10.1371/Journal.Pcbi.1002092 |
0.691 |
|
| 2011 |
Schiess M, Urbanczik R, Senn W. Reinforcement learning in dendritic structures Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P293 |
0.727 |
|
| 2011 |
Friedrich J, Urbanczik R, Senn W. Policy gradient rules for populations of spiking neurons Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P111 |
0.671 |
|
| 2011 |
Brea J, Senn W, Pfister JP. Sequence learning with hidden units in spiking neural networks Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011. |
0.572 |
|
| 2010 |
Friedrich J, Urbanczik R, Senn W. Learning spike-based population codes by reward and population feedback. Neural Computation. 22: 1698-717. PMID 20235820 DOI: 10.1162/Neco.2010.05-09-1010 |
0.662 |
|
| 2010 |
Fiete IR, Senn W, Wang CZ, Hahnloser RH. Spike-time-dependent plasticity and heterosynaptic competition organize networks to produce long scale-free sequences of neural activity. Neuron. 65: 563-76. PMID 20188660 DOI: 10.1016/j.neuron.2010.02.003 |
0.429 |
|
| 2009 |
Schäfer R, Vasilaki E, Senn W. Adaptive gain modulation in V1 explains contextual modifications during bisection learning. Plos Computational Biology. 5: e1000617. PMID 20019808 DOI: 10.1371/journal.pcbi.1000617 |
0.676 |
|
| 2009 |
Vasilaki E, Frémaux N, Urbanczik R, Senn W, Gerstner W. Spike-based reinforcement learning in continuous state and action space: when policy gradient methods fail. Plos Computational Biology. 5: e1000586. PMID 19997492 DOI: 10.1371/Journal.Pcbi.1000586 |
0.837 |
|
| 2009 |
Urbanczik R, Senn W. A gradient learning rule for the tempotron. Neural Computation. 21: 340-52. PMID 19431262 DOI: 10.1162/Neco.2008.09-07-605 |
0.711 |
|
| 2009 |
Vladimirskiy BB, Vasilaki E, Urbanczik R, Senn W. Stimulus sampling as an exploration mechanism for fast reinforcement learning. Biological Cybernetics. 100: 319-30. PMID 19360435 DOI: 10.1007/S00422-009-0305-X |
0.785 |
|
| 2009 |
Urbanczik R, Senn W. Reinforcement learning in populations of spiking neurons. Nature Neuroscience. 12: 250-2. PMID 19219040 DOI: 10.1038/Nn.2264 |
0.707 |
|
| 2009 |
Vasilaki E, Fusi S, Wang XJ, Senn W. Learning flexible sensori-motor mappings in a complex network. Biological Cybernetics. 100: 147-58. PMID 19153762 DOI: 10.1007/s00422-008-0288-z |
0.805 |
|
| 2009 |
Murayama M, Pérez-Garci E, Nevian T, Bock T, Senn W, Larkum ME. Dendritic encoding of sensory stimuli controlled by deep cortical interneurons. Nature. 457: 1137-41. PMID 19151696 DOI: 10.1038/nature07663 |
0.455 |
|
| 2009 |
Vasilaki E, Frémaux N, Urbanczik R, Senn W, Gerstner W. Correction: Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail Plos Computational Biology. 5. DOI: 10.1371/annotation/307ea250-3792-4ceb-b905-162d86c96baf |
0.701 |
|
| 2008 |
Giugliano M, La Camera G, Fusi S, Senn W. The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs. Biological Cybernetics. 99: 303-18. PMID 19011920 DOI: 10.1007/s00422-008-0270-9 |
0.833 |
|
| 2008 |
La Camera G, Giugliano M, Senn W, Fusi S. The response of cortical neurons to in vivo-like input current: theory and experiment : I. Noisy inputs with stationary statistics. Biological Cybernetics. 99: 279-301. PMID 18985378 DOI: 10.1007/s00422-008-0272-7 |
0.827 |
|
| 2008 |
Jolivet R, Roth A, Schürmann F, Gerstner W, Senn W. Special issue on quantitative neuron modeling. Biological Cybernetics. 99: 237-9. PMID 18985376 DOI: 10.1007/s00422-008-0274-5 |
0.63 |
|
| 2008 |
Kim Y, Vladimirskiy BB, Senn W. Modulating the granularity of category formation by global cortical States. Frontiers in Computational Neuroscience. 2: 1. PMID 18946531 DOI: 10.3389/neuro.10.001.2008 |
0.396 |
|
| 2008 |
Leibold C, Senn W. Special issue on object localization. Biological Cybernetics. 98: 447. PMID 18491158 DOI: 10.1007/s00422-008-0231-3 |
0.306 |
|
| 2008 |
Thurley K, Senn W, Lüscher HR. Dopamine increases the gain of the input-output response of rat prefrontal pyramidal neurons. Journal of Neurophysiology. 99: 2985-97. PMID 18400958 DOI: 10.1152/Jn.01098.2007 |
0.737 |
|
| 2008 |
Vasilaki E, Urbanczik R, Senn W, Gerstner W. Spike-based reinforcement learning of navigation Bmc Neuroscience. 9. DOI: 10.1186/1471-2202-9-S1-P72 |
0.795 |
|
| 2008 |
Vladimirskiy B, Senn W, Urbanczik R. A hierarchical predictive coding model of visual processing Bmc Neuroscience. 9. DOI: 10.1186/1471-2202-9-S1-P111 |
0.596 |
|
| 2007 |
Brader JM, Senn W, Fusi S. Learning real-world stimuli in a neural network with spike-driven synaptic dynamics. Neural Computation. 19: 2881-912. PMID 17883345 DOI: 10.1162/neco.2007.19.11.2881 |
0.782 |
|
| 2007 |
Schäfer R, Vasilaki E, Senn W. Perceptual learning via modification of cortical top-down signals. Plos Computational Biology. 3: e165. PMID 17715996 DOI: 10.1371/journal.pcbi.0030165 |
0.695 |
|
| 2006 |
Fusi S, Senn W. Eluding oblivion with smart stochastic selection of synaptic updates. Chaos (Woodbury, N.Y.). 16: 026112. PMID 16822044 DOI: 10.1063/1.2213587 |
0.711 |
|
| 2006 |
La Camera G, Rauch A, Thurbon D, Lüscher HR, Senn W, Fusi S. Multiple time scales of temporal response in pyramidal and fast spiking cortical neurons. Journal of Neurophysiology. 96: 3448-64. PMID 16807345 DOI: 10.1152/Jn.00453.2006 |
0.783 |
|
| 2005 |
Senn W, Fusi S. Learning only when necessary: better memories of correlated patterns in networks with bounded synapses. Neural Computation. 17: 2106-38. PMID 16105220 DOI: 10.1162/0899766054615644 |
0.77 |
|
| 2005 |
Senn W, Fusi S. Convergence of stochastic learning in perceptrons with binary synapses. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 71: 061907. PMID 16089765 DOI: 10.1103/PhysRevE.71.061907 |
0.735 |
|
| 2004 |
La Camera G, Rauch A, Lüscher HR, Senn W, Fusi S. Minimal models of adapted neuronal response to in vivo-like input currents. Neural Computation. 16: 2101-24. PMID 15333209 DOI: 10.1162/0899766041732468 |
0.792 |
|
| 2004 |
Larkum ME, Senn W, Lüscher HR. Top-down dendritic input increases the gain of layer 5 pyramidal neurons. Cerebral Cortex (New York, N.Y. : 1991). 14: 1059-70. PMID 15115747 DOI: 10.1093/cercor/bhh065 |
0.468 |
|
| 2004 |
Reutimann J, Yakovlev V, Fusi S, Senn W. Climbing neuronal activity as an event-based cortical representation of time. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 24: 3295-303. PMID 15056709 DOI: 10.1523/JNEUROSCI.4098-03.2004 |
0.738 |
|
| 2004 |
Senn W, Fusi S. Slow stochastic learning with global inhibition: A biological solution to the binary perceptron problem Neurocomputing. 58: 321-326. DOI: 10.1016/j.neucom.2004.01.062 |
0.759 |
|
| 2004 |
La Camera G, Senn W, Fusi S. Comparison between networks of conductance- and current-driven neurons: Stationary spike rates and subthreshold depolarization Neurocomputing. 58: 253-258. DOI: 10.1016/j.neucom.2004.01.052 |
0.815 |
|
| 2003 |
Berger T, Senn W, Lüscher HR. Hyperpolarization-activated current Ih disconnects somatic and dendritic spike initiation zones in layer V pyramidal neurons. Journal of Neurophysiology. 90: 2428-37. PMID 12801902 DOI: 10.1152/jn.00377.2003 |
0.382 |
|
| 2003 |
Rauch A, La Camera G, Luscher HR, Senn W, Fusi S. Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents. Journal of Neurophysiology. 90: 1598-612. PMID 12750422 DOI: 10.1152/jn.00293.2003 |
0.777 |
|
| 2003 |
Senn W, Buchs NJ. Spike-based synaptic plasticity and the emergence of direction selective simple cells: mathematical analysis. Journal of Computational Neuroscience. 14: 119-38. PMID 12567013 DOI: 10.1023/A:1021935100586 |
0.387 |
|
| 2002 |
Senn W. Beyond spike timing: the role of nonlinear plasticity and unreliable synapses. Biological Cybernetics. 87: 344-55. PMID 12461625 DOI: 10.1007/s00422-002-0350-1 |
0.452 |
|
| 2002 |
Carandini M, Heeger DJ, Senn W. A synaptic explanation of suppression in visual cortex. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 22: 10053-65. PMID 12427863 DOI: 10.1523/Jneurosci.22-22-10053.2002 |
0.392 |
|
| 2002 |
Buchs NJ, Senn W. Spike-based synaptic plasticity and the emergence of direction selective simple cells: simulation results. Journal of Computational Neuroscience. 13: 167-86. PMID 12226559 DOI: 10.1023/A:1020210230751 |
0.384 |
|
| 2002 |
Senn W, Schneider M, Ruf B. Activity-dependent development of axonal and dendritic delays, or, why synaptic transmission should be unreliable. Neural Computation. 14: 583-619. PMID 11860684 DOI: 10.1162/089976602317250915 |
0.465 |
|
| 2002 |
La Camera G, Rauch A, Senn W, Lüscher HR, Fusi S. Firing rate adaptation without losing sensitivity to input fluctuations Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2415: 180-185. |
0.751 |
|
| 2002 |
La Camera G, Fusi S, Senn W, Rauch A, Lüscher HR. When NMDA receptor conductances increase inter-spike interval variability Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2415: 235-240. |
0.721 |
|
| 2001 |
Senn W, Markram H, Tsodyks M. An algorithm for modifying neurotransmitter release probability based on pre- and postsynaptic spike timing. Neural Computation. 13: 35-67. PMID 11177427 DOI: 10.1162/089976601300014628 |
0.463 |
|
| 2001 |
Reutimann J, Fusi S, Senn W, Yakovlev V, Zohary E. A model of expectation effects in inferior temporal cortex Neurocomputing. 38: 1533-1540. DOI: 10.1016/S0925-2312(01)00551-3 |
0.698 |
|
| 2001 |
Buchs NJ, Senn W. Learning direction selectivity through spike-timing dependent modification of neurotransmitter release probability Neurocomputing. 38: 121-127. DOI: 10.1016/S0925-2312(01)00547-1 |
0.339 |
|
| 2000 |
Tabak J, Senn W, O'Donovan MJ, Rinzel J. Modeling of spontaneous activity in developing spinal cord using activity-dependent depression in an excitatory network. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 20: 3041-56. PMID 10751456 DOI: 10.1523/Jneurosci.20-08-03041.2000 |
0.61 |
|
| 2000 |
Senn W, Urbanczik R. Similar nonleaky integrate-and-fire neurons with instantaneous couplings always synchronize Siam Journal On Applied Mathematics. 61: 1143-1155. DOI: 10.1137/S0036139998346038 |
0.665 |
|
| 1999 |
Tabak J, Senn W, O'Donovan MJ, Rinzel J. Comparison of two models for pattern generation based on synaptic depression Neurocomputing. 26: 551-556. DOI: 10.1016/S0925-2312(99)00032-6 |
0.565 |
|
| 1998 |
Wannier T, Senn W. Recruitment of reticulospinal neurones and steady locomotion in lamprey. Neural Networks : the Official Journal of the International Neural Network Society. 11: 1005-1015. PMID 12662770 DOI: 10.1016/S0893-6080(98)00076-8 |
0.336 |
|
| 1998 |
Senn W, Wannier T, Kleinle J, Lüscher HR, Müller L, Streit J, Wyler K. Pattern generation by two coupled time-discrete neural networks with synaptic depression. Neural Computation. 10: 1251-75. PMID 9654770 DOI: 10.1162/089976698300017449 |
0.429 |
|
| 1998 |
Senn W, Segev I, Tsodyks M. Reading neuronal synchrony with depressing synapses. Neural Computation. 10: 815-9. PMID 9573406 DOI: 10.1162/089976698300017494 |
0.68 |
|
| 1996 |
Senn W, Wyler K, Streit J, Larkum M, Lüscher HR, Mey H, Müller L, Stainhauser D, Vogt K, Wannier T. Dynamics of a random neural network with synaptic depression Neural Networks. 9: 575-588. DOI: 10.1016/0893-6080(95)00109-3 |
0.774 |
|
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