Year |
Citation |
Score |
2022 |
Pietras B, Schmutz V, Schwalger T. Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity. Plos Computational Biology. 18: e1010809. PMID 36548392 DOI: 10.1371/journal.pcbi.1010809 |
0.763 |
|
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 |
0.596 |
|
2021 |
Schwalger T. Mapping input noise to escape noise in integrate-and-fire neurons: a level-crossing approach. Biological Cybernetics. 115: 539-562. PMID 34668051 DOI: 10.1007/s00422-021-00899-1 |
0.3 |
|
2020 |
Pietras B, Gallice N, Schwalger T. Low-dimensional firing-rate dynamics for populations of renewal-type spiking neurons. Physical Review. E. 102: 022407. PMID 32942450 DOI: 10.1103/PhysRevE.102.022407 |
0.488 |
|
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 |
0.74 |
|
2020 |
Pietras B, Gallice N, Schwalger T. Low-dimensional firing-rate dynamics for populations of renewal-type spiking neurons Physical Review E. 102. DOI: 10.1103/Physreve.102.022407 |
0.568 |
|
2019 |
Schwalger T, Chizhov AV. Mind the last spike - firing rate models for mesoscopic populations of spiking neurons. Current Opinion in Neurobiology. 58: 155-166. PMID 31590003 DOI: 10.1016/J.Conb.2019.08.003 |
0.553 |
|
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 |
0.704 |
|
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 |
0.707 |
|
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 |
0.737 |
|
2015 |
Schwalger T, Lindner B. Analytical approach to an integrate-and-fire model with spike-triggered adaptation. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 92: 062703. PMID 26764723 DOI: 10.1103/Physreve.92.062703 |
0.648 |
|
2015 |
Wieland S, Bernardi D, Schwalger T, Lindner B. Slow fluctuations in recurrent networks of spiking neurons. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 92: 040901. PMID 26565154 DOI: 10.1103/Physreve.92.040901 |
0.677 |
|
2015 |
Schwalger T, Droste F, Lindner B. Statistical structure of neural spiking under non-Poissonian or other non-white stimulation. Journal of Computational Neuroscience. 39: 29-51. PMID 25936628 DOI: 10.1007/S10827-015-0560-X |
0.726 |
|
2015 |
Shiau L, Schwalger T, Lindner B. Interspike interval correlation in a stochastic exponential integrate-and-fire model with subthreshold and spike-triggered adaptation. Journal of Computational Neuroscience. 38: 589-600. PMID 25894991 DOI: 10.1007/S10827-015-0558-4 |
0.705 |
|
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 |
0.716 |
|
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 |
0.774 |
|
2013 |
Schwalger T, Lindner B. Patterns of interval correlations in neural oscillators with adaptation. Frontiers in Computational Neuroscience. 7: 164. PMID 24348372 DOI: 10.3389/Fncom.2013.00164 |
0.679 |
|
2013 |
Bauermeister C, Schwalger T, Russell DF, Neiman AB, Lindner B. Characteristic effects of stochastic oscillatory forcing on neural firing: analytical theory and comparison to paddlefish electroreceptor data. Plos Computational Biology. 9: e1003170. PMID 23966844 DOI: 10.1371/Journal.Pcbi.1003170 |
0.706 |
|
2013 |
Droste F, Schwalger T, Lindner B. Interplay of two signals in a neuron with heterogeneous synaptic short-term plasticity. Frontiers in Computational Neuroscience. 7: 86. PMID 23882211 DOI: 10.3389/Fncom.2013.00086 |
0.602 |
|
2013 |
Schwalger T, Lindner B. Non-renewal spiking and neural dynamics - a simple theory of interspike-interval correlations in adapting neurons Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-O9 |
0.693 |
|
2013 |
Schwalger T, Miklody D, Lindner B. When the leak is weak – how the first-passage statistics of a biased random walk can approximate the ISI statistics of an adapting neuron The European Physical Journal Special Topics. 222: 2655-2666. DOI: 10.1140/Epjst/E2013-02045-4 |
0.697 |
|
2012 |
Fisch K, Schwalger T, Lindner B, Herz AV, Benda J. Channel noise from both slow adaptation currents and fast currents is required to explain spike-response variability in a sensory neuron. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 32: 17332-44. PMID 23197724 DOI: 10.1523/Jneurosci.6231-11.2012 |
0.671 |
|
2012 |
Schwalger T, Tiana-Alsina J, Torrent MC, Garcia-Ojalvo J, Lindner B. Interspike-interval correlations induced by two-state switching in an excitable system Epl (Europhysics Letters). 99: 10004. DOI: 10.1209/0295-5075/99/10004 |
0.57 |
|
2012 |
Droste F, Schwalger T, Lindner B. Heterogeneous short-term plasticity enables spectral separation of information in the neural spike train Bmc Neuroscience. 13. DOI: 10.1186/1471-2202-13-S1-P98 |
0.675 |
|
2011 |
Touya C, Schwalger T, Lindner B. Relation between cooperative molecular motors and active Brownian particles. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 83: 051913. PMID 21728577 DOI: 10.1103/Physreve.83.051913 |
0.587 |
|
2011 |
Schwalger T, Fisch K, Benda J, Lindner B. How stochastic adaptation of neurons shapes interspike interval statistics – theory and experiment Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P199 |
0.647 |
|
2010 |
Schwalger T, Fisch K, Benda J, Lindner B. How noisy adaptation of neurons shapes interspike interval histograms and correlations. Plos Computational Biology. 6: e1001026. PMID 21187900 DOI: 10.1371/Journal.Pcbi.1001026 |
0.673 |
|
2010 |
Schwalger T, Lindner B. Theory for serial correlations of interevent intervals European Physical Journal-Special Topics. 187: 211-221. DOI: 10.1140/Epjst/E2010-01286-Y |
0.608 |
|
2009 |
Schwalger T, Goedeke S, Diesmann M. Bifurcation analysis of synchronization dynamics in cortical feed-forward networks in novel coordinates Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P256 |
0.366 |
|
2009 |
Schwalger T, Lindner B. Serial interspike interval correlations of excitable neurons with memory Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P122 |
0.705 |
|
2008 |
Schwalger T, Lindner B. Higher-order statistics of a bistable system driven by dichotomous colored noise. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 78: 021121. PMID 18850800 DOI: 10.1103/Physreve.78.021121 |
0.604 |
|
2008 |
Schwalger T, Schimansky-Geier L. Interspike interval statistics of a leaky integrate-and-fire neuron driven by Gaussian noise with large correlation times. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 77: 031914. PMID 18517429 DOI: 10.1103/Physreve.77.031914 |
0.661 |
|
2008 |
Goedeke S, Schwalger T, Diesmann M. Theory of neuronal spike densities for synchronous activity in cortical feed-forward networks Bmc Neuroscience. 9: P143. DOI: 10.1186/1471-2202-9-S1-P143 |
0.585 |
|
2007 |
Lindner B, Schwalger T. Correlations in the sequence of residence times. Physical Review Letters. 98: 210603. PMID 17677758 DOI: 10.1103/Physrevlett.98.210603 |
0.603 |
|
2006 |
Schwalger T, Dzhanoev A, Loskutov A. May chaos always be suppressed by parametric perturbations? Chaos (Woodbury, N.Y.). 16: 023109. PMID 16822012 DOI: 10.1063/1.2195787 |
0.309 |
|
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