Theory of Complex Systems and Neurophysics

Benjamin Lindner
My interests lie in applications of methods from nonlinear dynamics, stochastic processes, and information theory to complex systems, in particular in neuroscience and cell biology. I focus on developing analytical frameworks for these problems, which does not exclude occasional (mostly happy) encounters with experimental data and serious attempts to understand them in theoretical terms.

030-2093-82492 or 030-2093-7934 | benjamin.lindner(at)physik.hu-berlin.de | Homepage

open position

030-2093-82492 or 030-2093-7934 | benjamin.lindner(at)physik.hu-berlin.de

Michael Zaks
I am interested in collective dynamics in ensembles of coupled systems that model physical and biological effects, like onset and breakdown of synchronous oscillations, symmetry breaking and mechanisms of clustering. Starting from simplified models and proceeding to more realistic ones, I apply methods of nonlinear dynamics to study the effects of coupling patterns, heterogeneity and system size upon transitions between different types of behavior.

030-2093-7608 | zaks(at)physik.hu-berlin.de

Ralf Tönjes
My primary research experience is on the effects of noise and heterogeneity in the transition to collective dynamics of coupled systems. As a Research Assistant in this group I try to contribute this experience to ongoing research projects and in support of the younger researchers.

030-2093-82480 | toenjes(at)physik.hu-berlin.de

Maria Schlungbaum
In my PhD thesis I study the signal detection in sensory neurons which are subject to two competing periodic signals of distinct frequencies and amplitudes. This kind of problem arises if we want to understand how several weakly electric fish communicate in natural situations, e.g. during courtship behavior (one female and two competing male fish). In collaboration with the experimental lab of Jan Benda in Tuebingen, I would like to understand how this cocktail-party problem is solved by the electro-sensory system of this species.

030-2093-82494 | maria.schlungbaum(at)bccn-berlin.de

Jakob Stubenrauch
I want to understand how cognitive functions emerge from the collective behavior of the large number of neurons in our brain. To this end, I study idealized neuronal networks using methods from statistical physics. In my PhD thesis, I focus on the consolidation of short-term memory into long-term memory, and I investigate how a knowledge base or schema can facilitate this process.

030-2093-82494 | jakob.stubenrauch(at)bccn-berlin.de

Georg Podhaisky
Stochastic oscillations are observed in a wide variety of systems. They occur at different length and time scales, ranging from cellular biology to climate science. While these noisy oscillatory dynamics can be caused by different mechanisms leading to qualitatively different statistics, Pérez-Cervera et al. (PNAS 120, 2023) demonstrated that a universal description can be obtained from an eigenfunction of the Kolmogorov backward operator. In my PhD thesis, I study this approach and its potential extensions. I also want to understand, how it can be applied to empirical data.

030-2093-82494 | georg.podhaisky(at)bccn-berlin.de

Kolja Klett
I'm studying fluctuation dissipation relations of the subthreshold voltage.

030-2093-82494 | kolja.klett(at)bccn-berlin.de

Johannes Abelardo Pineiro
Statistics of neurons are often measured in the Fourier domain and the employed neuron models are often analytically tractable only in the Fourier domain. For settings with a transient component this framework fails and it is very hard to connect experiment and theory. In my master thesis I investigate Ornstein-Uhlenbeck processes under transient stimuli and calculate their spectrograms. By understanding the benefits and challenges of this theory in a "simple" setting, we hope to extend this approach to more complex (nonlinear) integrate and fire neuron models.

Leander Dittrich
Neurons encode (sensory) stimuli into sequences of action potentials, known as spike trains. These spike trains also arise in the absence of stimuli. Analyzing the statistics of both regimes is necessary for understanding the displayed information filtering properties. My research focuses on fluctuation dissipation relations, which connect these statistics, in a two-stage neural network. The first stage consists of a population of neurons driven by a common stimulus. The second stage is a single neuron detecting simultaneous firing from those neurons. Similar networks comprise the initial stages of the neural processing chain for many sensory inputs.

Ole Kreißl
In my bachelors thesis I examine input output cross correlations for particles undergoing standard Brownian diffusion with stochastic resetting.

Laurenz Güntner
Many systems in physics, biology, and chemistry exhibit noisy, irregular oscillations. A universal framework using the eigenfunctions of the Kolmogorov backward operator has been put forward to describe these stochastic oscillators, regardless of their specific physical origin. My bachelor thesis tests the scope of this approach in the domain of systems with discrete states.