| Year |
Citation |
Score |
| 2023 |
Brea J, Clayton NS, Gerstner W. Computational models of episodic-like memory in food-caching birds. Nature Communications. 14: 2979. PMID 37221167 DOI: 10.1038/s41467-023-38570-x |
0.788 |
|
| 2022 |
Liakoni V, Lehmann MP, Modirshanechi A, Brea J, Lutti A, Gerstner W, Preuschoff K. Brain signals of a Surprise-Actor-Critic model: Evidence for multiple learning modules in human decision making. Neuroimage. 246: 118780. PMID 34875383 DOI: 10.1016/j.neuroimage.2021.118780 |
0.791 |
|
| 2021 |
Gastaldi C, Schwalger T, De Falco E, Quiroga RQ, Gerstner W. When shared concept cells support associations: Theory of overlapping memory engrams. Plos Computational Biology. 17: e1009691. PMID 34968383 DOI: 10.1371/journal.pcbi.1009691 |
0.733 |
|
| 2021 |
Xu HA, Modirshanechi A, Lehmann MP, Gerstner W, Herzog MH. Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making. Plos Computational Biology. 17: e1009070. PMID 34081705 DOI: 10.1371/journal.pcbi.1009070 |
0.795 |
|
| 2021 |
Esmaeili V, Tamura K, Muscinelli SP, Modirshanechi A, Boscaglia M, Lee AB, Oryshchuk A, Foustoukos G, Liu Y, Crochet S, Gerstner W, Petersen CCH. Rapid suppression and sustained activation of distinct cortical regions for a delayed sensory-triggered motor response. Neuron. PMID 34077741 DOI: 10.1016/j.neuron.2021.05.005 |
0.779 |
|
| 2021 |
Liakoni V, Modirshanechi A, Gerstner W, Brea J. Learning in Volatile Environments with the Bayes Factor Surprise. Neural Computation. 1-72. PMID 33400898 DOI: 10.1162/neco_a_01352 |
0.779 |
|
| 2020 |
Meissner-Bernard C, Tsai MC, Logiaco L, Gerstner W. Dendritic Voltage Recordings Explain Paradoxical Synaptic Plasticity: A Modeling Study. Frontiers in Synaptic Neuroscience. 12: 585539. PMID 33224033 DOI: 10.3389/fnsyn.2020.585539 |
0.802 |
|
| 2020 |
Surace SC, Pfister JP, Gerstner W, Brea J. On the choice of metric in gradient-based theories of brain function. Plos Computational Biology. 16: e1007640. PMID 32271761 DOI: 10.1371/Journal.Pcbi.1007640 |
0.774 |
|
| 2020 |
Schmutz V, Gerstner W, Schwalger T. Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity. Journal of Mathematical Neuroscience. 10: 5. PMID 32253526 DOI: 10.1186/S13408-020-00082-Z |
0.828 |
|
| 2019 |
Gastaldi C, Muscinelli S, Gerstner W. Optimal Stimulation Protocol in a Bistable Synaptic Consolidation Model. Frontiers in Computational Neuroscience. 13: 78. PMID 31798436 DOI: 10.3389/fncom.2019.00078 |
0.363 |
|
| 2019 |
Lehmann MP, Xu HA, Liakoni V, Herzog MH, Gerstner W, Preuschoff K. One-shot learning and behavioral eligibility traces in sequential decision making. Elife. 8. PMID 31709980 DOI: 10.7554/Elife.47463 |
0.791 |
|
| 2019 |
Illing B, Gerstner W, Brea J. Biologically plausible deep learning - But how far can we go with shallow networks? Neural Networks : the Official Journal of the International Neural Network Society. 118: 90-101. PMID 31254771 DOI: 10.1016/j.neunet.2019.06.001 |
0.818 |
|
| 2019 |
Muscinelli SP, Gerstner W, Schwalger T. How single neuron properties shape chaotic dynamics and signal transmission in random neural networks. Plos Computational Biology. 15: e1007122. PMID 31181063 DOI: 10.1371/Journal.Pcbi.1007122 |
0.77 |
|
| 2019 |
Seeholzer A, Deger M, Gerstner W. Stability of working memory in continuous attractor networks under the control of short-term plasticity. Plos Computational Biology. 15: e1006928. PMID 31002672 DOI: 10.1371/Journal.Pcbi.1006928 |
0.794 |
|
| 2018 |
Gerstner W, Lehmann M, Liakoni V, Corneil D, Brea J. Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules. Frontiers in Neural Circuits. 12: 53. PMID 30108488 DOI: 10.3389/fncir.2018.00053 |
0.816 |
|
| 2018 |
Martinolli M, Gerstner W, Gilra A. Multi-Timescale Memory Dynamics Extend Task Repertoire in a Reinforcement Learning Network With Attention-Gated Memory. Frontiers in Computational Neuroscience. 12: 50. PMID 30061819 DOI: 10.3389/Fncom.2018.00050 |
0.801 |
|
| 2018 |
Setareh H, Deger M, Gerstner W. Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation. Plos Computational Biology. 14: e1006216. PMID 29979674 DOI: 10.1371/journal.pcbi.1006216 |
0.433 |
|
| 2017 |
Deger M, Seeholzer A, Gerstner W. Multicontact Co-operativity in Spike-Timing-Dependent Structural Plasticity Stabilizes Networks. Cerebral Cortex (New York, N.Y. : 1991). PMID 29300903 DOI: 10.1093/Cercor/Bhx339 |
0.821 |
|
| 2017 |
Gilra A, Gerstner W. Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network. Elife. 6. PMID 29173280 DOI: 10.7554/Elife.28295 |
0.823 |
|
| 2017 |
Faraji M, Preuschoff K, Gerstner W. Balancing New against Old Information: The Role of Puzzlement Surprise in Learning. Neural Computation. 1-50. PMID 29064784 DOI: 10.1162/Neco_A_01025 |
0.798 |
|
| 2017 |
Setareh H, Deger M, Petersen CCH, Gerstner W. Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons. Frontiers in Computational Neuroscience. 11: 52. PMID 28690508 DOI: 10.3389/Fncom.2017.00052 |
0.445 |
|
| 2017 |
Zenke F, Gerstner W, Ganguli S. The temporal paradox of Hebbian learning and homeostatic plasticity. Current Opinion in Neurobiology. 43: 166-176. PMID 28431369 DOI: 10.1016/J.Conb.2017.03.015 |
0.833 |
|
| 2017 |
Schwalger T, Deger M, Gerstner W. Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size. Plos Computational Biology. 13: e1005507. PMID 28422957 DOI: 10.1371/Journal.Pcbi.1005507 |
0.762 |
|
| 2017 |
Zenke F, Gerstner W. Hebbian plasticity requires compensatory processes on multiple timescales. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 372. PMID 28093557 DOI: 10.1098/Rstb.2016.0259 |
0.815 |
|
| 2017 |
Keck T, Toyoizumi T, Chen L, Doiron B, Feldman DE, Fox K, Gerstner W, Haydon PG, Hübener M, Lee HK, Lisman JE, Rose T, Sengpiel F, Stellwagen D, Stryker MP, et al. Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 372. PMID 28093552 DOI: 10.1098/Rstb.2016.0158 |
0.786 |
|
| 2016 |
Muscinelli SP, Gerstner W, Brea J. Exponentially Long Orbits in Hopfield Neural Networks. Neural Computation. 1-27. PMID 27870611 DOI: 10.1162/NECO_a_00919 |
0.784 |
|
| 2016 |
Brito CS, Gerstner W. Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation. Plos Computational Biology. 12: e1005070. PMID 27690349 DOI: 10.1371/journal.pcbi.1005070 |
0.766 |
|
| 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, ... ... Gerstner W, et al. 25th Annual Computational Neuroscience Meeting: CNS-2016 Bmc Neuroscience. 17: 54. PMID 27534393 DOI: 10.1186/S12868-016-0283-6 |
0.688 |
|
| 2016 |
Kastner DB, Schwalger T, Ziegler L, Gerstner W. A Model of Synaptic Reconsolidation. Frontiers in Neuroscience. 10: 206. PMID 27242410 DOI: 10.3389/Fnins.2016.00206 |
0.805 |
|
| 2016 |
Mensi S, Hagens O, Gerstner W, Pozzorini C. Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons. Plos Computational Biology. 12: e1004761. PMID 26907675 DOI: 10.1371/journal.pcbi.1004761 |
0.46 |
|
| 2016 |
Brea J, Gerstner W. Does computational neuroscience need new synaptic learning paradigms? Current Opinion in Behavioral Sciences. 11: 61-66. DOI: 10.1016/j.cobeha.2016.05.012 |
0.429 |
|
| 2015 |
Frémaux N, Gerstner W. Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules. Frontiers in Neural Circuits. 9: 85. PMID 26834568 DOI: 10.3389/fncir.2015.00085 |
0.431 |
|
| 2015 |
Pozzorini C, Mensi S, Hagens O, Naud R, Koch C, Gerstner W. Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models. Plos Computational Biology. 11: e1004275. PMID 26083597 DOI: 10.1371/journal.pcbi.1004275 |
0.824 |
|
| 2015 |
Zenke F, Agnes EJ, Gerstner W. Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. Nature Communications. 6: 6922. PMID 25897632 DOI: 10.1038/Ncomms7922 |
0.811 |
|
| 2015 |
Ziegler L, Zenke F, Kastner DB, Gerstner W. Synaptic consolidation: from synapses to behavioral modeling. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 35: 1319-34. PMID 25609644 DOI: 10.1523/Jneurosci.3989-14.2015 |
0.805 |
|
| 2015 |
Schwalger T, Deger M, Gerstner W. Bridging spiking neuron models and mesoscopic population models - a general theory for neural population dynamics Bmc Neuroscience. 16. DOI: 10.1186/1471-2202-16-S1-P79 |
0.767 |
|
| 2015 |
Muscinelli SP, Gerstner W. A hierarchy of time scales supports unsupervised learning of behavioral sequences Bmc Neuroscience. 16. DOI: 10.1186/1471-2202-16-S1-P78 |
0.413 |
|
| 2015 |
Faraji MJ, Preuschoff K, Gerstner W. Surprise minimization as a learning strategy in neural networks Bmc Neuroscience. 16. DOI: 10.1186/1471-2202-16-S1-P77 |
0.805 |
|
| 2014 |
Deger M, Schwalger T, Naud R, Gerstner W. Fluctuations and information filtering in coupled populations of spiking neurons with adaptation. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 90: 062704. PMID 25615126 DOI: 10.1103/Physreve.90.062704 |
0.808 |
|
| 2014 |
Zenke F, Gerstner W. Limits to high-speed simulations of spiking neural networks using general-purpose computers. Frontiers in Neuroinformatics. 8: 76. PMID 25309418 DOI: 10.3389/Fninf.2014.00076 |
0.815 |
|
| 2014 |
Naud R, Bathellier B, Gerstner W. Spike-timing prediction in cortical neurons with active dendrites. Frontiers in Computational Neuroscience. 8: 90. PMID 25165443 DOI: 10.3389/fncom.2014.00090 |
0.823 |
|
| 2014 |
Hennequin G, Vogels TP, Gerstner W. Optimal control of transient dynamics in balanced networks supports generation of complex movements. Neuron. 82: 1394-406. PMID 24945778 DOI: 10.1016/J.Neuron.2014.04.045 |
0.804 |
|
| 2014 |
Tomm C, Avermann M, Petersen C, Gerstner W, Vogels TP. Connection-type-specific biases make uniform random network models consistent with cortical recordings. Journal of Neurophysiology. 112: 1801-14. PMID 24944218 DOI: 10.1152/Jn.00629.2013 |
0.828 |
|
| 2014 |
Jimenez Rezende D, Gerstner W. Stochastic variational learning in recurrent spiking networks. Frontiers in Computational Neuroscience. 8: 38. PMID 24772078 DOI: 10.3389/fncom.2014.00038 |
0.458 |
|
| 2014 |
Faraji MJ, Preuschoff K, Gerstner W. Neuromodulation by surprise: a biologically plausible model of the learning rate dynamics Bmc Neuroscience. 15. DOI: 10.1186/1471-2202-15-S1-P159 |
0.386 |
|
| 2014 |
Setareh H, Deger M, Gerstner W. The role of interconnected hub neurons in cortical dynamics Bmc Neuroscience. 15. DOI: 10.1186/1471-2202-15-S1-P158 |
0.471 |
|
| 2014 |
Seeholzer A, Deger M, Gerstner W. Stabilizing working memory in spiking networks with biologically plausible synaptic dynamics Bmc Neuroscience. 15. DOI: 10.1186/1471-2202-15-S1-P157 |
0.83 |
|
| 2014 |
Gerstner W, Kistler WM, Naud R, Paninski L. Neuronal dynamics: From single neurons to networks and models of cognition Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. 1-577. DOI: 10.1017/CBO9781107447615 |
0.8 |
|
| 2013 |
Lütcke H, Gerhard F, Zenke F, Gerstner W, Helmchen F. Inference of neuronal network spike dynamics and topology from calcium imaging data. Frontiers in Neural Circuits. 7: 201. PMID 24399936 DOI: 10.3389/Fncir.2013.00201 |
0.815 |
|
| 2013 |
Zenke F, Hennequin G, Gerstner W. Synaptic plasticity in neural networks needs homeostasis with a fast rate detector. Plos Computational Biology. 9: e1003330. PMID 24244138 DOI: 10.1371/Journal.Pcbi.1003330 |
0.81 |
|
| 2013 |
Friedmann S, Frémaux N, Schemmel J, Gerstner W, Meier K. Reward-based learning under hardware constraints-using a RISC processor embedded in a neuromorphic substrate. Frontiers in Neuroscience. 7: 160. PMID 24065877 DOI: 10.3389/Fnins.2013.00160 |
0.762 |
|
| 2013 |
Pozzorini C, Naud R, Mensi S, Gerstner W. Temporal whitening by power-law adaptation in neocortical neurons. Nature Neuroscience. 16: 942-8. PMID 23749146 DOI: 10.1038/nn.3431 |
0.809 |
|
| 2013 |
Frémaux N, Sprekeler H, Gerstner W. Reinforcement learning using a continuous time actor-critic framework with spiking neurons. Plos Computational Biology. 9: e1003024. PMID 23592970 DOI: 10.1371/journal.pcbi.1003024 |
0.826 |
|
| 2013 |
Pawlak V, Greenberg DS, Sprekeler H, Gerstner W, Kerr JN. Changing the responses of cortical neurons from sub- to suprathreshold using single spikes in vivo. Elife. 2: e00012. PMID 23359858 DOI: 10.7554/Elife.00012 |
0.818 |
|
| 2013 |
Rüter J, Sprekeler H, Gerstner W, Herzog MH. The silent period of evidence integration in fast decision making. Plos One. 8: e46525. PMID 23349660 DOI: 10.1371/journal.pone.0046525 |
0.776 |
|
| 2013 |
Hugues E, de Brito CSN, Gerstner W, Romo R, Deco G. A model of perceptual discrimination under sequential sensory evidence Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P102 |
0.467 |
|
| 2013 |
Logiaco L, Quilodran R, Gerstner W, Procyk E, Arleo A. Modulation of a decision-making process by spatiotemporal spike patterns decoding: evidence from spike-train metrics analysis and spiking neural network modeling Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P10 |
0.815 |
|
| 2012 |
Naud R, Gerstner W. Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram. Plos Computational Biology. 8: e1002711. PMID 23055914 DOI: 10.1371/journal.pcbi.1002711 |
0.822 |
|
| 2012 |
Gerstner W, Sprekeler H, Deco G. Theory and simulation in neuroscience. Science (New York, N.Y.). 338: 60-5. PMID 23042882 DOI: 10.1126/Science.1227356 |
0.785 |
|
| 2012 |
Hennequin G, Vogels TP, Gerstner W. Non-normal amplification in random balanced neuronal networks. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 86: 011909. PMID 23005454 DOI: 10.1103/Physreve.86.011909 |
0.817 |
|
| 2012 |
Markram H, Gerstner W, Sjöström PJ. Spike-timing-dependent plasticity: a comprehensive overview. Frontiers in Synaptic Neuroscience. 4: 2. PMID 22807913 DOI: 10.3389/fnsyn.2012.00002 |
0.319 |
|
| 2012 |
Avermann M, Tomm C, Mateo C, Gerstner W, Petersen CC. Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex. Journal of Neurophysiology. 107: 3116-34. PMID 22402650 DOI: 10.1152/Jn.00917.2011 |
0.829 |
|
| 2012 |
Rüter J, Marcille N, Sprekeler H, Gerstner W, Herzog MH. Paradoxical evidence integration in rapid decision processes. Plos Computational Biology. 8: e1002382. PMID 22359494 DOI: 10.1371/Journal.Pcbi.1002382 |
0.773 |
|
| 2012 |
Mensi S, Naud R, Pozzorini C, Avermann M, Petersen CC, Gerstner W. Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms. Journal of Neurophysiology. 107: 1756-75. PMID 22157113 DOI: 10.1152/Jn.00408.2011 |
0.819 |
|
| 2012 |
Herzog MH, Aberg KC, Frémaux N, Gerstner W, Sprekeler H. Perceptual learning, roving and the unsupervised bias. Vision Research. 61: 95-9. PMID 22119774 DOI: 10.1068/V110194 |
0.8 |
|
| 2012 |
Pawlak V, Greenberg DS, Sprekeler H, Gerstner W, Kerr JN. Author response: Changing the responses of cortical neurons from sub- to suprathreshold using single spikes in vivo Elife. DOI: 10.7554/Elife.00012.012 |
0.794 |
|
| 2012 |
Naud R, Gerstner W. The performance (and limits) of simple neuron models: Generalizations of the leaky integrate-and-fire model Computational Systems Neurobiology. 163-192. DOI: 10.1007/978-94-007-3858-4_6 |
0.802 |
|
| 2011 |
Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W. Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks. Science (New York, N.Y.). 334: 1569-73. PMID 22075724 DOI: 10.1126/Science.1211095 |
0.808 |
|
| 2011 |
Markram H, Gerstner W, Sjöström PJ. A history of spike-timing-dependent plasticity. Frontiers in Synaptic Neuroscience. 3: 4. PMID 22007168 DOI: 10.3389/fnsyn.2011.00004 |
0.45 |
|
| 2011 |
Naud R, Gerhard F, Mensi S, Gerstner W. Improved similarity measures for small sets of spike trains. Neural Computation. 23: 3016-69. PMID 21919785 DOI: 10.3389/Conf.Fncom.2010.51.00102 |
0.801 |
|
| 2011 |
Gerhard F, Pipa G, Lima B, Neuenschwander S, Gerstner W. Extraction of Network Topology From Multi-Electrode Recordings: Is there a Small-World Effect? Frontiers in Computational Neuroscience. 5: 4. PMID 21344015 DOI: 10.3389/Fncom.2011.00004 |
0.717 |
|
| 2011 |
Tomm C, Avermann M, Vogels T, Gerstner W, Petersen C. The influence of structure on the response properties of biologically plausible neural network models Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P30 |
0.824 |
|
| 2011 |
Brito CS, Gerstner W. General conditions for spiking neurons and plasticity rules to perform independent component analysis Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P124 |
0.783 |
|
| 2011 |
Gerhard F, Gerstner W. Efficient modeling of neural activity using coupled renewal processes Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P123 |
0.724 |
|
| 2011 |
Ziegler L, Gerstner W. Synaptic tagging and capture: a bridge from molecular to behaviour Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P122 |
0.384 |
|
| 2011 |
Hennequin G, Vogels T, Gerstner W. Fast and richly structured activity in cortical networks with local inhibition Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P121 |
0.82 |
|
| 2011 |
Zenke F, Hennequin G, Sprekeler H, Vogels TP, Gerstner W. Plasticity and stability in recurrent neural networks Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P120 |
0.821 |
|
| 2011 |
Mensi S, Naud R, Pozzorini C, Avermann M, Petersen CC, Gerstner W. Automatic characterization of three cortical neuron types reveals two distinct adaptation mechanisms Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P119 |
0.82 |
|
| 2010 |
Gerstner W. From hebb rules to spike-timing-dependent plasticity: a personal account. Frontiers in Synaptic Neuroscience. 2: 151. PMID 21423537 DOI: 10.3389/fnsyn.2010.00151 |
0.482 |
|
| 2010 |
Clopath C, Gerstner W. Voltage and Spike Timing Interact in STDP - A Unified Model. Frontiers in Synaptic Neuroscience. 2: 25. PMID 21423511 DOI: 10.3389/fnsyn.2010.00025 |
0.776 |
|
| 2010 |
Hennequin G, Gerstner W, Pfister JP. STDP in Adaptive Neurons Gives Close-To-Optimal Information Transmission. Frontiers in Computational Neuroscience. 4: 143. PMID 21160559 DOI: 10.3389/Fncom.2010.00143 |
0.828 |
|
| 2010 |
Frémaux N, Sprekeler H, Gerstner W. Functional requirements for reward-modulated spike-timing-dependent plasticity. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 30: 13326-37. PMID 20926659 DOI: 10.1523/JNEUROSCI.6249-09.2010 |
0.809 |
|
| 2010 |
Clopath C, Büsing L, Vasilaki E, Gerstner W. Connectivity reflects coding: a model of voltage-based STDP with homeostasis. Nature Neuroscience. 13: 344-52. PMID 20098420 DOI: 10.1038/nn.2479 |
0.821 |
|
| 2010 |
Sjöström J, Gerstner W. Spike-timing dependent plasticity Scholarpedia. 5: 1362. DOI: 10.4249/scholarpedia.1362 |
0.438 |
|
| 2010 |
Gerstner W. Spiking Neuron Models Encyclopedia of Neuroscience. 277-280. DOI: 10.1016/B978-008045046-9.01405-4 |
0.309 |
|
| 2010 |
Gerhard F, Gerstner W. Rescaling, thinning or complementing? on goodness-of-fit procedures for point process models and Generalized Linear Models Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010. |
0.612 |
|
| 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 |
0.802 |
|
| 2009 |
Gerstner W, Naud R. Neuroscience. How good are neuron models? Science (New York, N.Y.). 326: 379-80. PMID 19833951 DOI: 10.1126/science.1181936 |
0.817 |
|
| 2009 |
Luksys G, Gerstner W, Sandi C. Stress, genotype and norepinephrine in the prediction of mouse behavior using reinforcement learning. Nature Neuroscience. 12: 1180-6. PMID 19684590 DOI: 10.1038/Nn.2374 |
0.32 |
|
| 2009 |
Sheynikhovich D, Chavarriaga R, Strösslin T, Arleo A, Gerstner W. Is there a geometric module for spatial orientation? Insights from a rodent navigation model. Psychological Review. 116: 540-66. PMID 19618986 DOI: 10.1037/A0016170 |
0.797 |
|
| 2009 |
Naud R, Berger T, Bathellier B, Carandini M, Gerstner W. Quantitative Single-Neuron Modeling: Competition 2009 Frontiers in Neuroinformatics. 3. DOI: 10.3389/conf.neuro.11.2009.08.106 |
0.812 |
|
| 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 |
0.723 |
|
| 2009 |
Naud R, Bathellier B, Gerstner W. Spike-timing prediction in a neuron model with active dendrites Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P29 |
0.823 |
|
| 2009 |
Clopath C, Ziegler L, Buesing L, Vasilaki E, Gerstner W. Modeling plasticity across different time scales: the TagTriC model Bmc Neuroscience. 10: P192. DOI: 10.1186/1471-2202-10-S1-P192 |
0.81 |
|
| 2009 |
Clopath C, Büsing L, Vasilaki E, Gerstner W. Connectivity reflects coding: A model of voltage-based spike-timing-dependent-plasticity with homeostasis Nature Precedings. DOI: 10.1038/Npre.2009.3362.1 |
0.82 |
|
| 2009 |
Sprekeler H, Hennequin G, Gerstner W. Code-specific policy gradient rules for spiking neurons Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 1741-1749. |
0.782 |
|
| 2009 |
Clopath C, Longtin A, Gerstner W. An online Hebbian learning rule that performs independent component analysis Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. |
0.77 |
|
| 2008 |
Clopath C, Ziegler L, Vasilaki E, Büsing L, Gerstner W. Tag-trigger-consolidation: a model of early and late long-term-potentiation and depression. Plos Computational Biology. 4: e1000248. PMID 19112486 DOI: 10.1371/journal.pcbi.1000248 |
0.801 |
|
| 2008 |
Jolivet R, Schürmann F, Berger TK, Naud R, Gerstner W, Roth A. The quantitative single-neuron modeling competition. Biological Cybernetics. 99: 417-26. PMID 19011928 DOI: 10.1007/S00422-008-0261-X |
0.829 |
|
| 2008 |
Badel L, Lefort S, Berger TK, Petersen CC, Gerstner W, Richardson MJ. Extracting non-linear integrate-and-fire models from experimental data using dynamic I-V curves. Biological Cybernetics. 99: 361-70. PMID 19011924 DOI: 10.1007/S00422-008-0259-4 |
0.599 |
|
| 2008 |
Naud R, Marcille N, Clopath C, Gerstner W. Firing patterns in the adaptive exponential integrate-and-fire model. Biological Cybernetics. 99: 335-47. PMID 19011922 DOI: 10.1007/S00422-008-0264-7 |
0.822 |
|
| 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 |
0.8 |
|
| 2008 |
Badel L, Gerstner W, Richardson MJ. Spike-triggered averages for passive and resonant neurons receiving filtered excitatory and inhibitory synaptic drive. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 78: 011914. PMID 18763989 DOI: 10.1103/PhysRevE.78.011914 |
0.581 |
|
| 2008 |
Bathellier B, Carleton A, Gerstner W. Gamma oscillations in a nonlinear regime: a minimal model approach using heterogeneous integrate-and-fire networks. Neural Computation. 20: 2973-3002. PMID 18533817 DOI: 10.1162/neco.2008.11-07-636 |
0.773 |
|
| 2008 |
Morrison A, Diesmann M, Gerstner W. Phenomenological models of synaptic plasticity based on spike timing. Biological Cybernetics. 98: 459-78. PMID 18491160 DOI: 10.1007/S00422-008-0233-1 |
0.477 |
|
| 2008 |
Hermens F, Luksys G, Gerstner W, Herzog MH, Ernst U. Modeling spatial and temporal aspects of visual backward masking. Psychological Review. 115: 83-100. PMID 18211186 DOI: 10.1037/0033-295X.115.1.83 |
0.497 |
|
| 2008 |
Jolivet R, Kobayashi R, Rauch A, Naud R, Shinomoto S, Gerstner W. A benchmark test for a quantitative assessment of simple neuron models. Journal of Neuroscience Methods. 169: 417-24. PMID 18160135 DOI: 10.1016/J.Jneumeth.2007.11.006 |
0.816 |
|
| 2008 |
Badel L, Lefort S, Brette R, Petersen CC, Gerstner W, Richardson MJ. Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces. Journal of Neurophysiology. 99: 656-66. PMID 18057107 DOI: 10.1152/Jn.01107.2007 |
0.575 |
|
| 2008 |
Gerstner W. Spike-response model Scholarpedia. 3: 1343. DOI: 10.4249/scholarpedia.1343 |
0.34 |
|
| 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 |
0.756 |
|
| 2007 |
Herzog MH, Esfeld M, Gerstner W. Consciousness & the small network argument. Neural Networks : the Official Journal of the International Neural Network Society. 20: 1054-6. PMID 17900860 DOI: 10.1016/j.neunet.2007.09.001 |
0.499 |
|
| 2007 |
Toyoizumi T, Pfister JP, Aihara K, Gerstner W. Optimality model of unsupervised spike-timing-dependent plasticity: synaptic memory and weight distribution. Neural Computation. 19: 639-71. PMID 17298228 DOI: 10.1162/Neco.2007.19.3.639 |
0.822 |
|
| 2007 |
Marcille N, Clopath C, Ranjan R, Druckmann S, Schuermann F, Markram H, Gerstner W. Predicting neuronal activity with an adaptive exponential integrate-and-fire model Bmc Neuroscience. 8: P121. DOI: 10.1186/1471-2202-8-S2-P121 |
0.821 |
|
| 2007 |
Clopath C, Jolivet R, Rauch A, Lüscher HR, Gerstner W. Predicting neuronal activity with simple models of the threshold type: Adaptive Exponential Integrate-and-Fire model with two compartments Neurocomputing. 70: 1668-1673. DOI: 10.1016/J.Neucom.2006.10.047 |
0.821 |
|
| 2007 |
Lukšys G, Knüsel J, Sheynikhovich D, Sandi C, Gerstner W. Effects of stress and genotype on meta-parameter dynamics in reinforcement learning Advances in Neural Information Processing Systems. 937-944. |
0.777 |
|
| 2006 |
Pfister JP, Gerstner W. Triplets of spikes in a model of spike timing-dependent plasticity. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 26: 9673-82. PMID 16988038 DOI: 10.1523/Jneurosci.1425-06.2006 |
0.652 |
|
| 2006 |
Richardson MJ, Gerstner W. Statistics of subthreshold neuronal voltage fluctuations due to conductance-based synaptic shot noise. Chaos (Woodbury, N.Y.). 16: 026106. PMID 16822038 DOI: 10.1063/1.2203409 |
0.534 |
|
| 2006 |
Aviel Y, Gerstner W. From spiking neurons to rate models: a cascade model as an approximation to spiking neuron models with refractoriness. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 73: 051908. PMID 16802968 DOI: 10.1103/PhysRevE.73.051908 |
0.441 |
|
| 2006 |
Pfister JP, Toyoizumi T, Barber D, Gerstner W. Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning. Neural Computation. 18: 1318-48. PMID 16764506 DOI: 10.1162/Neco.2006.18.6.1318 |
0.82 |
|
| 2006 |
Jolivet R, Rauch A, Lüscher HR, Gerstner W. Predicting spike timing of neocortical pyramidal neurons by simple threshold models. Journal of Computational Neuroscience. 21: 35-49. PMID 16633938 DOI: 10.1007/S10827-006-7074-5 |
0.75 |
|
| 2006 |
Sheynikhovich D, Chavarriaga R, Strösslin T, Gerstner W. Adaptive sensory processing for efficient place coding Neurocomputing. 69: 1211-1214. DOI: 10.1016/J.Neucom.2005.12.078 |
0.798 |
|
| 2006 |
Badel L, Gerstner W, Richardson MJE. Dependence of the spike-triggered average voltage on membrane response properties Neurocomputing. 69: 1062-1065. DOI: 10.1016/j.neucom.2005.12.046 |
0.543 |
|
| 2005 |
Mayor J, Gerstner W. Signal buffering in random networks of spiking neurons: microscopic versus macroscopic phenomena. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 72: 051906. PMID 16383644 DOI: 10.1103/PhysRevE.72.051906 |
0.574 |
|
| 2005 |
Strösslin T, Sheynikhovich D, Chavarriaga R, Gerstner W. Robust self-localisation and navigation based on hippocampal place cells. Neural Networks : the Official Journal of the International Neural Network Society. 18: 1125-40. PMID 16263241 DOI: 10.1016/J.Neunet.2005.08.012 |
0.819 |
|
| 2005 |
Chavarriaga R, Strösslin T, Sheynikhovich D, Gerstner W. A computational model of parallel navigation systems in rodents. Neuroinformatics. 3: 223-41. PMID 16077160 DOI: 10.1385/Ni:3:3:223 |
0.795 |
|
| 2005 |
Brette R, Gerstner W. Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. Journal of Neurophysiology. 94: 3637-42. PMID 16014787 DOI: 10.1152/Jn.00686.2005 |
0.444 |
|
| 2005 |
Mayor J, Gerstner W. Noise-enhanced computation in a model of a cortical column. Neuroreport. 16: 1237-40. PMID 16012356 DOI: 10.1097/00001756-200508010-00021 |
0.587 |
|
| 2005 |
Richardson MJ, Melamed O, Silberberg G, Gerstner W, Markram H. Short-term synaptic plasticity orchestrates the response of pyramidal cells and interneurons to population bursts. Journal of Computational Neuroscience. 18: 323-31. PMID 15830168 DOI: 10.1007/S10827-005-0434-8 |
0.561 |
|
| 2005 |
Richardson MJ, Gerstner W. Synaptic shot noise and conductance fluctuations affect the membrane voltage with equal significance. Neural Computation. 17: 923-47. PMID 15829095 DOI: 10.1162/0899766053429444 |
0.435 |
|
| 2005 |
Toyoizumi T, Pfister JP, Aihara K, Gerstner W. Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission. Proceedings of the National Academy of Sciences of the United States of America. 102: 5239-44. PMID 15795376 DOI: 10.1073/Pnas.0500495102 |
0.832 |
|
| 2005 |
Hermens F, Luksys G, Gerstner W, Herzog MH. Visual backward masking: Feed-forward or recurrent? Journal of Vision. 5: 764-764. DOI: 10.1167/5.8.764 |
0.431 |
|
| 2005 |
Chavarriaga R, Strösslin T, Sheynikhovich D, Gerstner W. Competition between cue response and place response: A model of rat navigation behaviour Connection Science. 17: 167-183. DOI: 10.1080/09540090500138093 |
0.796 |
|
| 2005 |
Melamed O, Silberberg G, Markram H, Gerstner W, Richardson MJE. Subthreshold cross-correlations between cortical neurons: A reference model with static synapses Neurocomputing. 65: 685-690. DOI: 10.1016/J.Neucom.2004.10.098 |
0.588 |
|
| 2005 |
Sheynikhovich D, Chavarriaga R, Strösslin T, Gerstner W. Spatial representation and navigation in a bio-inspired robot Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3575: 245-264. DOI: 10.1007/11521082_15 |
0.801 |
|
| 2005 |
Strösslin T, Chavarriaga R, Sheynikhovich D, Gerstner W. Modelling path integrator recalibration using hippocampal place cells Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3696: 51-56. |
0.783 |
|
| 2005 |
Pfister JP, Gerstner W. Beyond pair-based STDP: A phenomenogical rule for spike triplet and frequency effects Advances in Neural Information Processing Systems. 1081-1088. |
0.506 |
|
| 2005 |
Jolivet R, Rauch A, Lüscher HR, Gerstner W. Integrate-and-Fire models with adaptation are good enough: Predicting spike times under random current injection Advances in Neural Information Processing Systems. 595-602. |
0.675 |
|
| 2005 |
Toyoizumi T, Pfister JP, Aihara K, Gerstner W. Spike-timing dependent plasticity and mutual information maximization for a spiking neuron model Advances in Neural Information Processing Systems. |
0.814 |
|
| 2004 |
Mayor J, Gerstner W. Transient information flow in a network of excitatory and inhibitory model neurons: role of noise and signal autocorrelation. Journal of Physiology, Paris. 98: 417-28. PMID 16289547 DOI: 10.1016/j.jphysparis.2005.09.009 |
0.603 |
|
| 2004 |
Jolivet R, Gerstner W. Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival. Journal of Physiology, Paris. 98: 442-51. PMID 16274972 DOI: 10.1016/j.jphysparis.2005.09.010 |
0.762 |
|
| 2004 |
Arleo A, Smeraldi F, Gerstner W. Cognitive navigation based on nonuniform Gabor space sampling, unsupervised growing networks, and reinforcement learning. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 15: 639-52. PMID 15384552 DOI: 10.1109/Tnn.2004.826221 |
0.76 |
|
| 2004 |
Jolivet R, Lewis TJ, Gerstner W. Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy. Journal of Neurophysiology. 92: 959-76. PMID 15277599 DOI: 10.1152/Jn.00190.2004 |
0.736 |
|
| 2004 |
Melamed O, Gerstner W, Maass W, Tsodyks M, Markram H. Coding and learning of behavioral sequences. Trends in Neurosciences. 27: 11-4; discussion 14-. PMID 14698603 DOI: 10.1016/J.Tins.2003.10.014 |
0.318 |
|
| 2004 |
Millán JdR, Renkens F, Mouriño J, Gerstner W. Brain-actuated interaction Artificial Intelligence. 159: 241-259. DOI: 10.1016/j.artint.2004.05.008 |
0.554 |
|
| 2004 |
Chavarriaga R, Gerstner W. Combining visual and proprioceptive information in a model of spatial learning and navigation Ieee International Conference On Neural Networks - Conference Proceedings. 1: 603-608. |
0.564 |
|
| 2003 |
Tonnelier A, Gerstner W. Piecewise linear differential equations and integrate-and-fire neurons: insights from two-dimensional membrane models. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 67: 021908. PMID 12636716 DOI: 10.1103/Physreve.67.021908 |
0.36 |
|
| 2003 |
Jolivet R, Lewis TJ, Gerstner W. The spike response model: A framework to predict neuronal spike trains Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2714: 846-853. |
0.706 |
|
| 2003 |
Mayor J, Gerstner W. Online processing of multiple inputs in a sparsely-connected recurrent neural network Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2714: 839-845. |
0.529 |
|
| 2003 |
Pfister JP, Barber D, Gerstner W. Optimal hebbian learning: A probabilistic point of view Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2714: 92-98. |
0.518 |
|
| 2002 |
Gerstner W, Kistler WM. Mathematical formulations of Hebbian learning. Biological Cybernetics. 87: 404-15. PMID 12461630 DOI: 10.1007/s00422-002-0353-y |
0.433 |
|
| 2002 |
Herrmann A, Gerstner W. Noise and the PSTH response to current transients: II. Integrate-and-fire model with slow recovery and application to motoneuron data. Journal of Computational Neuroscience. 12: 83-95. PMID 12053155 DOI: 10.1023/A:1015739523224 |
0.373 |
|
| 2002 |
Kistler WM, Gerstner W. Stable propagation of activity pulses in populations of spiking neurons. Neural Computation. 14: 987-97. PMID 11972904 DOI: 10.1162/089976602753633358 |
0.44 |
|
| 2002 |
Strösslin T, Krebser C, Arleo A, Gerstner W. Combining multimodal sensory input for spatial learning Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2415: 87-92. |
0.733 |
|
| 2001 |
Spiridon M, Gerstner W. Effect of lateral connections on the accuracy of the population code for a network of spiking neurons. Network (Bristol, England). 12: 409-21. PMID 11762897 DOI: 10.1088/0954-898X/12/4/301 |
0.425 |
|
| 2001 |
Herrmann A, Gerstner W. Noise and the PSTH response to current transients: I. General theory and application to the integrate-and-fire neuron. Journal of Computational Neuroscience. 11: 135-51. PMID 11717530 DOI: 10.1023/A:1012841516004 |
0.356 |
|
| 2001 |
Kempter R, Gerstner W, van Hemmen JL. Intrinsic stabilization of output rates by spike-based Hebbian learning. Neural Computation. 13: 2709-41. PMID 11705408 DOI: 10.1162/089976601317098501 |
0.763 |
|
| 2001 |
Gerstner W. Coding properties of spiking neurons: reverse and cross-correlations. Neural Networks : the Official Journal of the International Neural Network Society. 14: 599-610. PMID 11665756 DOI: 10.1016/S0893-6080(01)00053-3 |
0.391 |
|
| 2001 |
Gerstner W. Chapter 12 A framework for spiking neuron models: The spike response model Handbook of Biological Physics. 4: 469-516. DOI: 10.1016/S1383-8121(01)80015-4 |
0.315 |
|
| 2001 |
Arleo A, Gerstner W. Spatial orientation in navigating agents: Modeling head-direction cells Neurocomputing. 38: 1059-1065. DOI: 10.1016/S0925-2312(01)00572-0 |
0.747 |
|
| 2001 |
Spiridon M, Gerstner W. The accuracy of the population vector estimate in networks of integrate-and-fire type neurons using stationary and transient stimuli Neurocomputing. 38: 927-934. DOI: 10.1016/S0925-2312(01)00399-X |
0.372 |
|
| 2001 |
Arleo A, Smeraldi F, Hug S, Gerstner W. Place cells and spatial navigation based on 2D visual feature extraction, path integration, and reinforcement learning Advances in Neural Information Processing Systems. |
0.713 |
|
| 2000 |
Arleo A, Gerstner W. Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity. Biological Cybernetics. 83: 287-99. PMID 11007302 DOI: 10.1007/S004220000171 |
0.763 |
|
| 2000 |
Gerstner W. Population dynamics of spiking neurons: fast transients, asynchronous states, and locking. Neural Computation. 12: 43-89. PMID 10636933 DOI: 10.1162/089976600300015899 |
0.382 |
|
| 2000 |
Spiridon M, Chow CC, Gerstner W. Effect of correlations on signal transmission in a population of spiking neurons Neurocomputing. 32: 529-535. DOI: 10.1016/S0925-2312(00)00209-5 |
0.378 |
|
| 2000 |
Plesser HE, Gerstner W. Escape rate models for noisy integrate-and-fire neurons Neurocomputing. 32: 219-224. DOI: 10.1016/S0925-2312(00)00167-3 |
0.412 |
|
| 2000 |
Herrmann A, Gerstner W. Effect of noise on neuron transient response Neurocomputing. 32: 147-154. DOI: 10.1016/S0925-2312(00)00156-9 |
0.358 |
|
| 1999 |
Spiridon M, Gerstner W. Noise spectrum and signal transmission through a population of spiking neurons. Network (Bristol, England). 10: 257-72. PMID 10496476 DOI: 10.1088/0954-898X/10/3/304 |
0.351 |
|
| 1999 |
Kempter R, Gerstner W, Van Hemmen JL. Hebbian learning and spiking neurons Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics. 59: 4498-4514. DOI: 10.1103/Physreve.59.4498 |
0.764 |
|
| 1999 |
Kempter R, Gerstner W, Van Hemmen JL. Spike-based compared to rate-based hebbian learning Advances in Neural Information Processing Systems. 125-131. |
0.698 |
|
| 1998 |
Wimbauer S, Gerstner W, van Hemmen JL. Analysis of a correlation-based model for the development of orientation-selective receptive fields in the visual cortex. Network (Bristol, England). 9: 449-66. PMID 10221574 DOI: 10.1088/0954-898X_9_4_004 |
0.564 |
|
| 1998 |
Kempter R, Gerstner W, van Hemmen JL. How the threshold of a neuron determines its capacity for coincidence detection. Bio Systems. 48: 105-12. PMID 9886637 DOI: 10.1016/S0303-2647(98)00055-0 |
0.739 |
|
| 1998 |
Kempter R, Gerstner W, van Hemmen JL, Wagner H. Extracting oscillations. Neuronal coincidence detection with noisy periodic spike input. Neural Computation. 10: 1987-2017. PMID 9804669 DOI: 10.1162/089976698300016945 |
0.749 |
|
| 1997 |
Gerstner W, Kreiter AK, Markram H, Herz AV. Neural codes: firing rates and beyond. Proceedings of the National Academy of Sciences of the United States of America. 94: 12740-1. PMID 9398065 DOI: 10.1073/pnas.94.24.12740 |
0.679 |
|
| 1997 |
Gerstner W, Abbott LF. Learning navigational maps through potentiation and modulation of hippocampal place cells. Journal of Computational Neuroscience. 4: 79-94. PMID 9046453 DOI: 10.1023/A:1008820728122 |
0.658 |
|
| 1997 |
Kistler WM, Gerstner W, Van Hemmen JL. Reduction of the Hodgkin-Huxley Equations to a Single-Variable Threshold Model Neural Computation. 9: 1015-1045. DOI: 10.1162/Neco.1997.9.5.1015 |
0.62 |
|
| 1996 |
Gerstner W, van Hemmen JL, Cowan JD. What matters in neuronal locking? Neural Computation. 8: 1653-76. PMID 8888612 DOI: 10.1162/Neco.1996.8.8.1653 |
0.598 |
|
| 1996 |
Fuentes U, Ritz R, Gerstner W, Van Hemmen JL. Vertical signal flow and oscillations in a three-layer model of the cortex. Journal of Computational Neuroscience. 3: 125-36. PMID 8840229 DOI: 10.1007/BF00160808 |
0.559 |
|
| 1996 |
Gerstner W, Kempter R, van Hemmen JL, Wagner H. A neuronal learning rule for sub-millisecond temporal coding. Nature. 383: 76-81. PMID 8779718 DOI: 10.1038/383076a0 |
0.769 |
|
| 1995 |
Gerstner W. Time structure of the activity in neural network models. Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics. 51: 738-758. PMID 9962697 DOI: 10.1103/PhysRevE.51.738 |
0.45 |
|
| 1995 |
Fohlmeister C, Gerstner W, Ritz R, van Hemmen JL. Spontaneous excitations in the visual cortex: stripes, spirals, rings, and collective bursts. Neural Computation. 7: 905-14. PMID 7584892 DOI: 10.1162/Neco.1995.7.5.905 |
0.589 |
|
| 1995 |
Schiegg A, Gerstner W, Ritz R, van Hemmen JL. Intracellular Ca2+ stores can account for the time course of LTP induction: a model of Ca2+ dynamics in dendritic spines. Journal of Neurophysiology. 74: 1046-55. PMID 7500131 DOI: 10.1152/Jn.1995.74.3.1046 |
0.501 |
|
| 1994 |
Ritz R, Gerstner W, Fuentes U, van Hemmen JL. A biologically motivated and analytically soluble model of collective oscillations in the cortex. II. Application to binding and pattern segmentation. Biological Cybernetics. 71: 349-58. PMID 7948226 DOI: 10.1007/BF00239622 |
0.621 |
|
| 1994 |
Wimbauer S, Gerstner W, van Hemmen JL. Emergence of spatiotemporal receptive fields and its application to motion detection Biological Cybernetics. 72: 81-92. DOI: 10.1007/BF00206240 |
0.556 |
|
| 1993 |
Gerstner W, Ritz R, van Hemmen JL. A biologically motivated and analytically soluble model of collective oscillations in the cortex. I. Theory of weak locking. Biological Cybernetics. 68: 363-74. PMID 8386552 DOI: 10.1007/BF00201861 |
0.637 |
|
| 1993 |
Gerstner W, Ritz R, van Hemmen JL. Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns. Biological Cybernetics. 69: 503-15. PMID 7903867 DOI: 10.1007/BF00199450 |
0.634 |
|
| 1992 |
Gerstner W, van Hemmen JL. Universality in neural networks: the importance of the 'mean firing rate'. Biological Cybernetics. 67: 195-205. PMID 1498186 DOI: 10.1007/BF00204392 |
0.643 |
|
| 1992 |
Gerstner W, van Hemmen JL. Associative memory in a network of 'spiking' neurons Network: Computation in Neural Systems. 3: 139-164. DOI: 10.1088/0954-898X_3_2_004 |
0.537 |
|
| 1992 |
Gerstner W, Hemmen JV. Associative memory in a network of ‘spiking’ neurons Network: Computation in Neural Systems. 3: 139-164. DOI: 10.1088/0954-898X/3/2/004 |
0.47 |
|
| 1970 |
Gerstner W. The power and limits of simple neuron models: predicting neural activity spike by spike Frontiers in Neuroinformatics. DOI: 10.3389/Conf.Neuro.11.2009.08.131 |
0.417 |
|
| 1970 |
Naud R, Gerstner W, Mensi S, Becker T. Complexity and performance in simple neuron models Frontiers in Neuroinformatics. DOI: 10.3389/Conf.Neuro.11.2009.08.087 |
0.804 |
|
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