WAN - Invited Speakers

Abstract: To Be Announced...

Old stuff and new stuff about automata networks and their update modes

An automata network is classically defined as a set of entities (the automata) which interact with each other over discrete time. More formally, an automata network composed of n automata is a collection of n local functions which define how each automaton computes its state based on the states of those influencing it. Research on these objects has shown that, given an automata network, distinct dynamics can be associated with it depending on how the updates of the automaton states are organized over time (i.e., the update modes). By restricting ourselves to Boolean networks and periodic update modes, we will discuss in this presentation some of the main results highlighting the importance of not ignoring update modes when studying automata networks, whether in the computational context or in the modeling context.

Investigating spatio-temporal patterns in biological neural networks

Simple excitable network models play an important role in recent efforts to better understand organizational and functional principles of biological neurological systems. Cognitive processes, e.g., seem to be characterized by emerging activity patterns in densely connected networks. The architecture of these networks can be plastic, with the node activity shaping structural changes. In this talk, I will spotlight different approaches to modeling and analyzing such networks using discrete excitable models, namely, Boolean and susceptible-excited refractory (SER) models. The focus of the dynamic analysis is on uncovering properties of the attractor landscape, synchronization effects and parameter dependencies. A further point of interest is the shaping of the network structure through reorganization principles rooted in topological but also functional system properties. Lastly, system control approaches are used to identify targets for topological changes destroying undesired dynamical patterns corresponding to cognitive dysfunction. Mainly, these investigations consist of simulation studies without a focus on uncovering underlying mathematical principles or exploiting available theory. I will aim at identifying some starting points for tackling more rigorous results in the context of neural network modeling as well as formulate more general research questions motivated by the phenomena encountered in application.