Max Planck Institute for Dynamics and Self-Organization -- Department for Nonlinear Dynamics and Network Dynamics Group
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Self-organization in Adaptive Systems

Head of group:  Geisel, Theo 

The following serves as an overview over the projects and members in the group "Self-organization in Adaptive Systems" at the Max Planck Institute for Dynamics and Self-Organization.


Long range correlations and 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. 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.

Fluctuations in human musical rhythms

Music generated by computers and rhythm machines sometimes sounds unnatural. One reason for this is the absence of small inaccuracies that are part of every human activity. Professional audio software therefore offers a so-called humanizing technique, by which the regularity of musical rhythms can be randomized to some extent. But what exactly is the nature of the inaccuracy in human musical rhythms? Studying this question for the first time, we found that the temporal rhythmic fluctuations exhibit scale-free long-range correlations, i.e., a small rhythmic fluctuation at some point in time does not only influence fluctuations shortly thereafter, but even after tens of seconds. While this characterization is relevant for neurophysiological mechanisms of timing, it also leads to a novel concept for humanizing musical sequences. Comparing with conventionally humanized versions listeners showed a high preference for long-range correlated humanized music over uncorrelated humanized music.

Self-Organized Criticality in the Activity Dynamics of Neural Networks

Neural networks display characteristics of critical dynamics in the neural activities as theoretically predicted. The power-law statistics for the size of avalanches of neural activity was confirmed in real neurons, where the critical behavior is re-approached even after a substantial perturbation of the parameters of the system. These findings provide evidence for the presence of self-organized criticality (SOC). We study neural network models that exhibit power-law statistics with realistic synaptic mechanisms, such as synaptic depression.

People working in this Group:

Name Email Phone
Theo Geisel send email   [+49-(0)551-5176-400

Cooperation partners