Biography2001-2006 Diploma course in Physics at Humboldt University Berlin, Germany
2003/2004 Study of Physics at Lomonosov Moscow State University, Russia
2007-2008 Postgraduate research at RIKEN Brain Science Institute, Wako-shi, Japan
2008-2013 PhD studies in Theoretical Physics with Benjamin Lindner at the Max-Planck Institute for the Physics of Complex Systems, Dresden and Bernstein Center for Computational Neuroscience, Berlin, Germany
since 2013 Postdoc with Wulfram Gerstners at EPFL, Lausanne, Switzerland
MissionIn my research, I want to gain a theoretical understanding of how sensations, thoughts and actions emerge from the complex interactions of millions of nerve cells in the brain. Towards that goal, I develop new theoretical methods to bridge different scales in the brain, from the microscopic level of spiking neurons to the mesoscopic level of neuronal populations all the way to the macroscopic level of large-scale brain activity as seen in EEG, MEG or fMRI data. For this multiscale approach, I use mathematical tools from statistical physics, stochastic processes and nonlinear dynamics, such as mean-field theory, coarse-graining and dimensionality reduction techniques. Our theoretical framework will enable us to better understand the widely observed phenomena of neural variability and correlations in cortical circuits in terms of their biophysical mechanisms and their functional role for information processing.
Current work* multiscale theory of neural activity
* non-renewal point processes
* stochastic integrate-and-fire neuron models with adaptation and/or colored noise
* dynamics of spiking neural networks
V. Schmutz; W. Gerstner; T. Schwalger : Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity; Journal Of Mathematical Neuroscience. 2020-04-06. DOI : 10.1186/s13408-020-00082-z.
S. P. Muscinelli; W. Gerstner; T. Schwalger : How single neuron properties shape chaotic dynamics and signal transmission in random neural networks; PLoS Computational Biology. 2019-06-01. DOI : 10.1371/journal.pcbi.1007122.
T. Schwalger; M. Deger; W. Gerstner : Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size; PLoS Computational Biology. 2017. DOI : 10.1371/journal.pcbi.1005507.
D. B. Kastner; T. Schwalger; L. Ziegler; W. Gerstner : A Model of Synaptic Reconsolidation; Frontiers in Neuroscience. 2016. DOI : 10.3389/fnins.2016.00206.
T. Schwalger; B. Lindner : Analytical approach to an integrate-and-fire model with spike-triggered adaptation; Physical Review E. 2015. DOI : 10.1103/PhysRevE.92.062703.
S. Wieland; D. Bernardi; T. Schwalger; B. Lindner : Slow fluctuations in recurrent networks of spiking neurons; Physical Review E. 2015. DOI : 10.1103/PhysRevE.92.040901.
L. Shiau; T. Schwalger; B. Lindner : Interspike interval correlation in a stochastic exponential integrate-and-fire model with subthreshold and spike-triggered adaptation; Journal Of Computational Neuroscience. 2015. DOI : 10.1007/s10827-015-0558-4.
T. Schwalger; F. Droste; B. Lindner : Statistical structure of neural spiking under non-Poissonian or other non-white stimulation; Journal of Computational Neuroscience. 2015. DOI : 10.1007/s10827-015-0560-x.
M. Deger; T. Schwalger; R. Naud; W. Gerstner : Fluctuations and information filtering in coupled populations of spiking neurons with adaptation; Physical Review E. 2014. DOI : 10.1103/PhysRevE.90.062704.
C. Bauermeister; T. Schwalger; D. F. Russell; A. B. Neiman; B. Lindner : Characteristic Effects of Stochastic Oscillatory Forcing on Neural Firing: Analytical Theory and Comparison to Paddlefish Electroreceptor Data; PLoS Computational Biology. 2013. DOI : 10.1371/journal.pcbi.1003170.
T. Schwalger; B. Lindner : Patterns of interval correlations in neural oscillators with adaptation; Frontiers In Computational Neuroscience. 2013. DOI : 10.3389/fncom.2013.00164.
T. Schwalger; J. Tiana-Alsina; M. C. Torrent; J. Garcia-Ojalvo; B. Lindner : Interspike-interval correlations induced by two-state switching in an excitable system; EPL (Europhysics Letters). 2012. DOI : 10.1209/0295-5075/99/10004.
C. Touya; T. Schwalger; B. Lindner : Relation between cooperative molecular motors and active Brownian particles; Physical Review E. 2011. DOI : 10.1103/PhysRevE.83.051913.
T. Schwalger; L. Schimansky-Geier : Interspike interval statistics of a leaky integrate-and-fire neuron driven by Gaussian noise with large correlation times; Physical Review E. 2008. DOI : 10.1103/PhysRevE.77.031914.
T. Schwalger; B. Lindner : Higher-order statistics of a bistable system driven by dichotomous colored noise; Physical Review E. 2008. DOI : 10.1103/PhysRevE.78.021121.
B. Lindner; T. Schwalger : Correlations in the Sequence of Residence Times; Physical Review Letters. 2007. DOI : 10.1103/PhysRevLett.98.210603.
T. Schwalger; A. Dzhanoev; A. Loskutov : May chaos always be suppressed by parametric perturbations?; Chaos: An Interdisciplinary Journal of Nonlinear Science. 2006. DOI : 10.1063/1.2195787.