Combining networks with nonlinear dynamics, biological applications and information parity
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ABSTRACT / Computational neuroscience comes in many flavors, which take different perspectives to describe processes and understand mechanisms in the brain. In my presentation, I will highlight a number of these directions for the study of neuronal systems. We will visit modelling attempts that are informed by empirical data, e.g., from neuroimaging measurements, to study synchrony in the brain [1-3]. We will also explore how nonlinear dynamics and network science can work together to uncover plausible points of operation and biological functions such as locomotion of the nematode Caenorhabditis elegans [4]. Finally, we will look into the concept of information parity, which is a measure inspired from information theory to quantify consonance of influence among nodes with respect to the whole network architecture [5].
References:
[1] V. Vuksanović and P. Hövel: Functional connectivity of distant cortical regions: role of remote synchronization and symmetry in interactions, NeuroImage 97, 1 (2014).
[2] V. Vuksanović and P. Hövel: Dynamic changes in network synchrony reveal resting-state functional networks, Chaos 25, 023116 (2015).
[3] P. Hövel, A. Viol, P. Loske, L. Merfort, and V. Vuksanović: Synchronization in functional networks of the human brain, J. Nonlinear Sci. 30, 2259 (2020).
[4] T. Maertens, E. Schöll, J. Ruiz, and P. Hövel: Multilayer network analysis of C. elegans: Looking into the locomotory circuitry, Neurocumputing 427, 238 (2021).
[5] A. Viol, V. Vuksanović, and P. Hövel: Information parity in complex networks, Physica A 561, 125233 (2021).
BIO / Dr. Philipp Hövel is a trained mathematician and physicist, who currently works as a permanent lecturer in Applied Mathematics (School of Mathematical Sciences at University College Cork, Ireland). He received his PhD and Habilitation from Technische Universität Berlin (Germany) in 2009 and 2017, respectively. From 2011 to 2013, he joined the Center for Complex Network Research (Northeastern University, Boston, USA) as a postdoctoral researcher. At the same time and continued after his return to Germany, he led a Junior Research Group at the Bernstein Center for Computational Neuroscience Berlin.
Philipp’s research mission is to lift the boundaries between data-oriented science, theoretical approaches, and numerical simulations addressing interdisciplinary questions based on an overlap of nonlinear dynamics, network science, and control theory. His areas of mathematical expertise include complex systems, bifurcation theory, delay, differential equations, and complex networks. Besides mathematical modelling and analytical investigations, he has always looked for experimental validation of his theoretical findings and a combination of the models with empirical data. His interdisciplinary research concept has led to better insight and fundamental understanding of synchronization processes and other dynamical phenomena. In addition, the combination with empirical data sets will allow for investigations of real-world relevance in areas, such as neuroscience, epidemiology and beyond.