Source: http://www.nld.ds.mpg.de/people/priesemann
Timestamp: 2019-04-25 02:58:01+00:00

Document:
Max Planck Research Group Leader "Neural Systems Theory"
I. Subsampling. In most large networks, it is impossible to sample the activity of all nodes in parallel. For example, the human brain comprises 80 billion neurons, but current techniques allow sampling the activity from only a few hundred neurons at a time. I showed for collective states of networks that subsampling can severely impede inferences about the properties of the full system. In detail, subsampling in critical models can distort the expected power law relations, and thereby a critical system can be misinterpreted as sub- or super-critical. I am currently developing an approach to overcome subsampling effects by extending methods from finite size scaling theory.
II. Subcritical Dynamics in vivo. In neuroscience, a popular hypothesis is that the collective neural dynamics should self-organize to a critical state, because at criticality simple models maximize their information processing capacities. However, criticality also comes with the risk of spontaneous runaway activity (epilepsy). I recently obtained the first evidence that neural dynamics maintains a distance to criticality, and thus can keep a safety margin to runaway activity. This distance to criticality is consistent across different species, but changes with vigilance states. Currently, I refine the methods to quantify the distance to criticality, with the aim to understand how this distance is adjusted depending on needs.
III. Information theory. To understand information processing in the brain, I co-developed an open source toolbox (TRENTOOL) that is specialized on estimating information theoretic quantities from neural recordings. Using this toolbox, I showed that transfer entropy is capable of detecting the interaction delay and the direction of information flow between brain areas, using my own turtle ex vivo recordings. Currently, the toolbox is extended to also quantify active information storage, and return the time resolved versions of both, active information storage and transfer.
IV. Music. Long range correlations dominate neural activity. Therefore music, which is considered the mirror of the soul and the brain, should also reflect these correlations. In collaboration with Theo Geisel, we currently investigate how long range correlations and information theoretic quantities change with genres, and whether long range correlations play a central role in making Swing swing.
We acknowledge support by the Max Planck Society, the Bernstein Center for Computational Neuroscience (BMBF), the German-Israel Foundation (GIF), the Science without Borders Program, and the Gertrud Reemtsma Foundation.
We are welcoming applications from outstanding, highly motivated students and strong researchers with a background in physics, neuroscience or related fields. Applications should be addressed to Viola Priesemann and include a letter of motivation, CV, transcript of records, and the contact of two referees.
M. Wibral, J. Lizier, V. Priesemann, "Bits from Brains for Biologically Inspried Computing", Front. Robotics and AI, 2015.
V. Priesemann, et al., “Spike avalanches in vivo suggest a driven, slightly subcritical brain state,” Front. Syst. Neurosci., 2014.
M. Wibral, J. Lizier, S. Vögler, V. Priesemann, and R. Galuske, “Local active information storage as a tool to understand distributed neural information processing,” Front. Neuroinformatics, 2014.
V. Priesemann, M. Valderrama, M. Wibral, and M. Le Van Quyen, “Neuronal Avalanches Differ from Wakefulness to Deep Sleep–Evidence from Intracranial Depth Recordings in Humans,” PLoS Comput. Biol., 2013.
M. Wibral, N. Pampu, V. Priesemann, F. Siebenhühner, H. Seiwert, M. Lindner, J. T. Lizier, and R. Vicente, “Measuring information-transfer delays,” PloS One, 2013.
M. Lindner, R. Vicente, V. Priesemann, and M. Wibral, “TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy,” BMC Neurosci., 2011.
V. Priesemann, M. H. Munk, and M. Wibral, “Subsampling effects in neuronal avalanche distributions recorded in vivo,” BMC Neurosci., 2009.

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