1. Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity Schmutz, V., Gerstner, W., and Schwalger, T. J. Math. Neurosc. 2020 [Abstract] [DOI] [PDF]
  2. Mind the Last Spike – Firing Rate Models for Mesoscopic Populations of Spiking Neurons Schwalger, T., and Chizhov, A. V. Curr. Opin. Neurobiol. 2019 [Abstract] [DOI] [PDF]
  3. How single neuron properties shape chaotic dynamics and signal transmission in random neural networks Muscinelli, S. P., Gerstner, W., and Schwalger, T. PLoS Comput. Biol. 2019 [Abstract] [DOI] [PDF] [Code]
  4. Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity Schmutz, V, Gerstner, W., and Schwalger, T. arXiv e-prints 2018 [Abstract] [PDF]
  5. Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size Schwalger, T., Deger, M., and Gerstner, W. PLoS Comput. Biol. 2017 [Abstract] [DOI] [PDF] [Code]
  6. A model of synaptic reconsolidation Kastner*, D. B., Schwalger, T.*, Ziegler, L., and Gerstner, W. Front. Neurosci. 2016 [Abstract] [DOI] [PDF]
  7. Analytical approach to an integrate-and-fire model with spike-triggered adaptation Schwalger, T., and Lindner, B. Phys. Rev. E 2015 [Abstract] [DOI] [PDF]
  8. Statistical structure of neural spiking under non-Poissonian or other non-white stimulation Schwalger, T., Droste, F., and Lindner, B. J. Comput. Neurosci. 2015 [Abstract] [DOI] [PDF]
  9. Slow fluctuations in recurrent networks of spiking neurons. Wieland, S., Bernardi, D., Schwalger, T., and Lindner, B. Phys. Rev. E 2015 [Abstract] [DOI] [PDF]
  10. Interspike interval correlation in a stochastic exponential integrate-and-fire model with subthreshold and spike-triggered adaptation. Shiau, L., Schwalger, T., and Lindner, B. J. Comput. Neurosci. 2015 [Abstract] [DOI] [PDF]
  11. Fluctuations and information filtering in coupled populations of spiking neurons with adaptation Deger*, M., Schwalger*, T., Naud, R., and Gerstner, W. Phys. Rev. E 2014 [Abstract] [DOI] [PDF] [Code]
  12. Patterns of interval correlations in neural oscillators with adaptation Schwalger, T., and Lindner, B. Front. Comput. Neurosci. 2013 [Abstract] [DOI] [PDF]
  13. When the leak is weak – how the first-passage statistics of a biased random walk can approximate the ISI statistics of an adapting neuron Schwalger, T., Miklody, D., and Lindner, B. Eur. Phys. J. Spec. Topics 2013 [Abstract] [DOI] [PDF]
  14. Characteristic Effects of Stochastic Oscillatory Forcing on Neural Firing Statistics: Theory and Application to Paddlefish Electroreceptor Afferents Bauermeister*, C., Schwalger*, T., Russell, D.F., Neiman, A.B., and Lindner, B. PLoS Comp. Biol. 2013 [Abstract] [DOI] [PDF]
  15. Interplay of two signals in a neuron with heterogeneous synaptic short-term plasticity. Droste, F., Schwalger, T., and Lindner, B. Front. Comp. Neurosci. 2013 [Abstract] [DOI] [PDF]
  16. Interspike-interval correlations induced by two-state switching in an excitable system Schwalger, T., Tiana-Alsina, J., Torrent, M. C., Garcia-Ojalvo, J., and Lindner, B. EPL 2012 [Abstract] [DOI] [PDF]
  17. Channel noise from both slow adaptation currents and fast currents is required to explain spike-response variability in a sensory neuron. Fisch, K., Schwalger, T., Lindner, B., Herz, A.V.M., and Benda, J. J. Neurosci. 2012 [Abstract] [DOI] [PDF]
  18. Relation between cooperative molecular motors and active Brownian particles Touya, C., Schwalger, T., and Lindner, B. Phys. Rev. E 2011 [Abstract] [DOI] [PDF]
  19. Theory for serial correlations of interevent intervals Schwalger, T., and Lindner, B. Eur. Phys. J.-Spec. Top. 2010 [Abstract] [DOI] [PDF]
  20. How Noisy Adaptation of Neurons Shapes Interspike Interval Histograms and Correlations Schwalger, T., Fisch, K., Benda, J., and Lindner, B. PLoS Comput Biol 2010 [Abstract] [DOI] [PDF] [Code]
  21. Bifurcation analysis of synchronization dynamics in cortical feed-forward networks in novel coordinates Schwalger, T., Goedeke, S., and Diesmann, M. BMC Neurosci 2009
  22. Higher-order statistics of a bistable system driven by dichotomous colored noise Schwalger, T., and Lindner, L. Phys. Rev. E 2008 [Abstract] [DOI] [PDF]
  23. Interspike interval statistics of a leaky integrate-and-fire neuron driven by Gaussian noise with large correlation times Schwalger, T., and Schimansky-Geier, L. Phys. Rev. E 2008 [Abstract] [DOI] [PDF]
  24. Theory of neuronal spike densities for synchronous activity in cortical feed-forward networks Goedeke, S., Schwalger, T., and Diesmann, M. BMC Neurosci 2008
  25. Correlations in the Sequence of Residence Times Lindner, B., and Schwalger, T. Phys. Rev. Lett. 2007 [Abstract] [DOI] [PDF]
  26. May chaos always be suppressed by parametric perturbations? Schwalger, T., Dzhanoev, A., and Loskutov, A. Chaos 2006 [Abstract] [DOI]