Titles and abstracts
NeVAE: A Deep Generative Model for Molecular Graphs
Niloy Ganguly, IIT Kharagpur, India
Neuronal Networks as Discrete Dynamical Systems and
Dynamical Mechanisms of Information Routing in the Brain
Theo Geisel, Max Planck Institute for Dynamics and Self-Organization, Göttingen & Bernstein Center for Computational Neuroscience Göttingen, Germany.
Work in collaboration with A. Palmigiano, D. Battaglia, and F. Wolf
 A. Palmigiano et al., Nature Neurosci. 20, 1014 (2017)
Topological determinants of excitation propagation and self-sustained activity on excitable networks
Annick Lesne, CNRS, LPTMC (Paris) & IGMM (Montpellier), France.
Work done with M.-T. Hütt (Jacobs Univ., Bremen, Germany) and C. Hilgetag (Hamburg Univ., Germany & Boston Univ., USA)
Discrete models of excitable dynamics on graphs are an efficient framework for studying the interplay between network topology and dynamics. This topic is of practical relevance to diverse fields, ranging from neuroscience to engineering. In particular, we have investigated how a single excitation propagates through a random network as a function of the excitation threshold, that is, the relative amount of activity in the neighborhood required for the excitation of a node. We observe that two sharp transitions delineate a region of sustained activity. Using analytical considerations and numerical simulation, we show that these transitions originate from the presence of barriers to propagation and the excitation of topological cycles, respectively, and can be predicted from the network topology. Our findings are interpreted in the context of self-sustained activity in neural systems, which is a question of long-standing interest in computational neuroscience.
Mathematical modeling of mechanical signaling in biological development
Roeland Merks, Leiden university, Netherlands
Population Genetics and Democratic Elections
Johannes Müller, TU München, Germany. Joint work with Volker Hösel, TUM, and Aurelien Tellier, TUM.
In recent years it became clear that the voter model can be used to model democratic elections. One point needs to be adapted: The Voter model on a connected, finite graph tends in the long run to an absorbing state, where only one opinion/party is left. Therefore, different forms of the noisy voter model have been introduced [1,2], where individuals choose with a certain probability one of the given opinions, without interaction with neighbours.
We propose a slightly different approach, based on the infinite allele model, that is well known in population genetics. With a certain probability, new groups are created, and old groups disappear. We show that this model (with slight adaptations to avoid opinions/parties with only few supporters) on a full graph has properties that are in line with election data from the US, Netherlands, France and Germany .
In the present talk we investigate the importance of the graph structure, with the aim to better understand mechanisms behind the variance in election data.
1. Granovsky B.L., Madras N. (1995) The noisy voter model. Stoch. Process. Their. Appl. , 55:2343
2. Braha D., de Aguiar M. (2017) Voting contagion: Modeling and analysis of a century of U.S. presidential elections. PLoS ONE, 12:e0177970
3. Hösel, V., Müller, J. Tellier, A. (2019) Universality of neutral models: decision process in politics. Palgrave Comm., in press.
From Pixelsex to Mathematical Socialism – confronting cellular automata with real (artistic) live
Cellular automata (CA) modelling often applies a similar method like art history: visual analogies. Such analogies are made comparing a pattern generated in silico with patterns in vivo respectively in ‘physica’. An inverse approach is to create physically automata and look for eventual implications of this real world implementation. In my presentation I will introduce a couple of my CA related artistic and composition works revealing a certain shift related to such a material translation by physico-mechanical apparatuses or human agents.
For instance I realized performances with musicians or dancers creating sound or spatiotemporal patterns reacting simply on their neighbours. Human actors reveal special forms of interferences as they can simply fail to execute correctly a neighbourhood rule. Such a failure is not necessarily aleatoric, but can show also certain patterns, as a recent project in India revealed creating hand knotted carpets according to CA rules – this was a very special social experiment at the very same time.
The most challenging task was to use automata for music composition. For a water organ I introduced a special way to analyse the dynamics of the agents: The activity of each pipe is translated into alternating water levels changing also the pipe’s timbre and pitch. Last but not least I will report about some collateral effects of such an artistic approach playing for instance with asymmetric topologies or developing a new way to describe the dynamics and robustness of automata system complementary to the Wolfram classification.
Future Mobility : Self-Organization, Inefficiencies and Paradoxa
Marc Timme, Chair for Network Dynamics, TU Dresden
Models, simulations (and real world data) here bridge the regimes between few, discrete entities and infinite numbers of entities characterized, e.g., by continuous flows. First, I highlight why and how hysteresis persistently causes major inefficiencies across ride-hauling (taxis, ride sharing etc.)systems. Second, I illustrate how ride pooling may enable smoother door-to-door service without the need of owning a private car, also illustrating a recent pilot project we organized. Finally, I point out how tech-enabled routing systems may be optimized not for fastest individual route but for overall effectiveness if the routes of many travelers collectively minimize the total time wasted. I am happy to discuss the long list of open
questions on future mobility.
This is work with Malte Schroeder, Philip Marszal, David Storch and others.