Geisel, Theo, Prof. Dr.

Professor of Theoretical Physics; Director, Max Planck Institute for Dynamics and Self-Organization; Coordinator, Bernstein Center for Computational Neuroscience

• 1975 Dr. rer. nat., University of Regensburg
• 1983 – 1987 Heisenberg fellow
• 1988 – 1989 Professor of Theoretical Physics, University of Würzburg
• 1989 – 1996 Professor of Theoretical Physics, University of Frankfurt
• since 1996 Professor of Theoretical Physics (em. since 2016), University of Göttingen
• since 1996 Director (Director em. since 2016), Max Planck Institute for Dynamics and Self-Organization, Göttingen
• 2004 – 2013 Founder and Coordinator, Bernstein Center for Computational Neuroscience, Göttingen

Major Research Interests

How do the myriads of neurons in our cortex cooperate when we perceive an object or perform another task? How do they self-organize in the preceding learning process? Questions like these address the complex dynamics of spatially extended and multicomponent nonlinear systems, which still reserve many surprises. In networks of sufficiently many spiking neurons e.g. we find unstable attractors, a phenomenon which would neither have been guessed nor understood without mathematical modelling and which many physicists consider an oxymoron. They can provide a neuronal network with a high degree of flexibility to adapt to permanently changing tasks. The tools and mathematical methods developed in studies of chaotic behaviour in the past can now help us clarify the dynamics and function of complex networks and spatially extended systems and reveal the biological role of dynamical phenomena like unstable attractors.

These methods lend themselves to applications in neuroscience from the level of single cells to the level of cell assemblies and large cortical networks, from the time scales of action potentials (milliseconds) to the time scales of learning and long-term memory (up to years). My work in the past has dealt among others with studies of stochastic resonance of single neurons under periodic and endogenous stimulation, detailed investigations of the properties, functions, and conditions of neuronal synchronization, and the development of neuronal maps in the visual cortex. We have elucidated the influence of the network topology on synchronization and other dynamical properties and demonstrated the existence of speed limits to network synchronization due to disordered connectivity. Besides, I am also focusing on other applications of nonlinear dynamics, e.g. for quantum chaos in semiconductor nanostructures and in mathematical models for the description and forecast of the spread of epidemics.

Figure 1: Basins of attraction of synchronized states in a network of spiking neurons. These unstable attactors allow rapid switching and give the network a high degree of flexibility.

• Nelias C, Sturm EM, Albrecht T, Hagmayer Y, Geisel T (2022) Downbeat Delays are a Key Component of Swing in Jazz. Commun. Phys. 5: 237


• Datseris G, Geisel T, Fleischmann R (2019) Robustness of Ballistic Transport in Antidot Superlattices. New J. Phys. 21: 043051


• Datseris G, Ziereis A, Albrecht T, Hagmayer Y, Priesemann V, Geisel T (2019) Microtiming Deviations and Swing Feel in Jazz. Scient. Rep. 9: 1


• Sogorsk Mi, Geisel T, Priesemann V (2018) Correlated Microtiming Deviations in Jazz and Rock Music. PLoS ONE 13(1):e0186361


• Palmigiano A, Geisel T, Wolf F, Battaglia D (2017) Flexible Information Routing by Transient Synchrony. Nature Neurosc. 20(7):1014


• Degueldre H, Metzger JJ, Geisel T, Fleischmann R (2016) Random Focusing of Tsunami Waves. Nature Physics 12:259-262


• Metzger JJ, Fleischmann R, Geisel T (2014) Statistics of Extreme Waves in Random Media. Phys. Rev. Lett. 112:203903


• Stollmeier F, Geisel T, Nagler J (2014) Prossible Origin of Stagnation and Variabitlity of Earth's Biodiversity. Phys. Rev. Lett. 112:228101