PhD position in Theoretical Neuroscience available
Projects
Connectivity and Dynamics
How does the connectivity of a network affect its dynamics? Obviously, individual network elements will act independently, if they are not connected. When connections are introduced, then the elements become dependent of each other, but not necessarily as a function of physical proximity. The fact, if two elements are connected or not, becomes more important for the function and dynamics of the network, than their location in space. The same situation is encountered in the brain. Here millions of neurons are connected in a complex manner, which determines how they can exchange information. In particular the timing of their information exchange is most important for the information processing in the brain. This research project asks fundamental questions on how the connectivity of a neural network determines the dynamics of information processing in the brain. In close collaboration with the Brain Network Recovery Group (Brain NRG) utilizing non-invasive brain imaging techniques including EEG, MEG and fMRI, we bridge the gaps between computational neuroscience and applied/clinical brain experimentation. Investigators: Viktor Jirsa, Arpan Banerjee, Murad Qubbaj, Roxanna Stefanescu
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Theoretical Foundations of Coordination Dynamics
Researchers in the field of Coordination Dynamics aim to identify general laws of pattern formation in human movements rather than searching for a locus of movement pattern generation. This approach is inspired by ideas borne by the theory of Dynamic Systems and Self-organization. In particular, living systems display a low-dimensional dynamics which can be interpreted as a form of information processing. Researchers in the field of cognition have devoted much time in uncovering the phenomenological laws underlying cognitive architectures with applications in pattern recognition, category formation and in general artificial intelligence. Similar efforts have been made in the field of Coordination Dynamics, but have been primarily limited to rhythmic movements. Though such an approach has its virtues such as the simplicity of the analysis and interpretation of the data, its limitations with respect to the understanding of more complex movements and potential applications in the real world are obvious. The goal of our project is to overcome these limitations and develop systematically a dynamic basis for the phenomenology of arbitrary complex human movements. Investigators: Viktor Jirsa, Arpan Banerjee
Cognitive Architectures: Integration and Segregation in Auditory Streaming
We aim to capture the perceptual dynamics of auditory streaming using a neurally inspired model of auditory processing. Traditional approaches view streaming as a competition of streams, realized within a tonotopically organized neural network. In contrast, we view streaming to be a dynamic integration process which resides at locations other than the sensory specific neural subsystems. This process finds its realization in the synchronization of neural ensembles or in the existence of informational convergence zones. Our approach uses two interacting dynamical systems, in which the first system responds to incoming acoustic stimuli and transforms them into a spatiotemporal neural field dynamics. The second system is a classification system, which is coupled to the neural fields and evolves dynamically to a stationary state. These states are identified with a single perceptual stream or multiple streams. Several results in human perception are modelled including temporal coherence and fission boundaries (van Noorden 1975), and crossing of motions (Bregman 1990). Our model predicts phenomena such as the existence of two streams with the same pitch, which cannot be explained by the traditional stream competition models. An experimental behavioral and functional MRI study is performed to provide proof of existence of this phenomenon.Investigators: Viktor Jirsa, Felix Almonte, Silke Dodel
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Team Dynamics Evolves Along Low-Dimensional Manifolds
What is specific about team interaction and how can it be measured? Such question addresses the essential nature of human social interactions, but despite being a presence in daily life, it has not been answered satisfactorily. A team consisting of one excellent and several poor members may score reasonably well by conventional standards, but may not in terms of team interaction. Our aim is to develop a metric by which the quality of team interaction can be assessed without the knowledge of an outcome (such as a score in a basketball match). This requires a quantitative analysis of team interaction for which we use a dynamical systems approach. In this approach at every point in time each team can be described by a set of variables (location of the team members, head orientations, etc.) and their temporal derivatives. Taken together, these variables form a team vector, that, across time, passes through a trajectory in a high-dimensional phase space. Multiple trajectories from the team performing the same task over and over again, lie in some manifold in phase space. The conceptual idea is that the properties of the manifold inform us about the quality of team interaction. For instance we expect the manifold to be low-dimensional due constraints imposed by team interaction. In our presentation we will introduce our approach using data from teams of soldiers performing a room clearing task.Investigators: Viktor Jirsa, Silke Dodel, Ajay Pillai
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Brain Imaging & Brain Dynamics
Observations of phenomena in neuroscience range from fractions of milliseconds of ion channel dynamics to days and months of memory formation in the temporal domain. In physical space, observations range from the molecular level to brain patterns of several centimeters size observed in EEG and MEG.
This vast complexity in space and in time can be only studied and understood under the guidance of theoretical models which allow the identification of the underlying mechanism of the experimentally observed phenomena. Such models may be developed for the same phenomena on different levels of description. For example, in the mid 1980s bimanual coordination dynamics has been understood in terms of nonlinearly coupled oscillators describing the periodic motion of limbs phenomenologically. In the late 1990s it became possible to derive this behavioral model from models which describe the processes in the brain during bimanual correlation, i.e. its neural correlate. Such traverse of scales of organization proves to be powerful, since individual descriptions of brain and behavioral dynamics do not have to stand on their own, but together tell a whole story on how brain and behavior determine each other.Investigators: Viktor Jirsa, Felix Almonte, Ajay Pillai, Silke Dodel, Arpan Banerjee
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