High frequency activity and behaviour

We are interested in how the brain integrates information at different physiological levels. We investigate whether the brain makes predictions at all and how we manage to create a model of the rules on which our environment is based. An interesting question is how attention puts the brain in a critical self-organized state so that information can be optimally processed.

On the behavioural and neurophysiological level we investigate whether impulsivity can be explained by differences in attention. Furthermore, we investigate how attention fluctuates over time and test the influence of these fluctuations on perception, consciousness and learning.

  • Head

    Head

    Stefan Dürschmid studied education and psychology in Magdeburg. After his doctoral thesis with Jochem Rieger and Hermann Hinrichs, he was a visiting scholar in the working group of Robert Knight at the University of California in Berkeley from 2013-2014, where he also spent regular working stays in recent years.

    In addition, he has a long-term cooperation with the Hebrew University of Jerusalem (Prof. Leon Deouell) and collaborates with Prof. Giulio Tononi (University of Wisconsin, Madison).

    In addition to his fascination with neuroscience, he is an enthusiastic violinist with several chamber music ensembles.

  • Members

    Members

    Head  
    Dr. Stefan Dürschmid+49 391-6263-92531stefan.duerschmid@med.ovgu.de
    PhD students  
    David Eckert  
    Andre Maric  
    Students  
    Anneke Bies  
    Lena Vogelgesang  
    Christian Wienke  

     

  • Projects

    Projects

    We are interested how the brain integrates information at different physiological levels.

    Predictive coding – promoted in recent year as the primary function of the brain – considers the brain a 'predictive machine' tuned to predict sensory information or motor outcomes.  Despite intense interest in the predictive capacity of the brain, there is no consensus on how prediction is implemented and controlled by different cortical brain structures. We analyze cortical responses in the tradition of using prediction error (PE) signals as a signature of predictive coding. However, whereas PE signals are a good proxy for predictions, what the field is missing to establish predictive coding directly, is true predictive (prospective) signals – signals that precede an event and indicate active anticipation of a particular event. That is, PE signals may be elicited with no active pre-stimulus anticipatory predictions involved if every stimulus is compared retrospectively, once encoded, to extant passive ‘memory models’. Critically, while many studies found indirect evidence for predictive coding in the form of PE signals, direct evidence for the actual prediction is hard to find. Most importantly, how the brain gains insight into regularities of the environment by representing statistical information to form such predictions is unclear.

    At a cortical network level, the functioning of the human brain requires the coordinated electrical activity of neurons well-tuned to process information. It has been suggested that neuronal activity at rest is organized in avalanches of events in which event size show no characteristic scale and displays critical dynamics. Such dynamics are associated with optimal information processing even though the brain is not capable of processing all available information at once. A fundamental mechanism to selectively enhance the information integration of certain stimuli is provided by attention. However, it remains unclear how attention improves information capacity and whether it affects the coordinated electrical activity of neurons. Given that human resting activity displays criticality and focused attention helps optimize information processing, we study avalanche distribution of feedback and feedforward networks during attention modulation.

    At a behavioral level we test whether and how behavioral relevant information are integrated. Impulsive decisions are proposed to arise from the tendency to overvalue smaller but sooner rewards or from being an insufficient consideration of objective values. In our group we investigate on a fine-grained temporal scale behavioral and oscillatory dynamics supporting impulsive decisions in conditions varying in the value for themselves and consequently in the amount of reward. Based on eye tracking and MEG recordings, we identify how differences in scrutiny of choice evaluation and hence the level of information integration predicts differences in discounting.

    At a consciousness level we study subjective awareness which is governed by fluctuations of the mind (mind wandering) and probe its impact on perception and meta-cognition and their MEG/EEG correlates.

  • Selected Publications

    Selected Publications

    Dürschmid S, Reichert C, Hinrichs H, Heinze HJ, Kirsch HE, Knight RT, Deouell LY.  Direct evidence for prediction signals in frontal cortex independent of prediction error. Cereb Cortex. 2018.

    Dürschmid S, Reichert C, Kuhn J, Freund HJ, Hinrichs H, Heinze HJ. Deep Brain stimulation of the Nucleus Basalis of Meynert attenuates early EEG components associated with defective sensory gating in patients with Alzheimer disease – a two-case study. EJN. 2017. Available from: 10.1111/ejn.13749

    Dürschmid S, Edwards E, Reichert C, Dewar C, Hinrichs H, Heinze HJ, Kirsch HE,Dalal SS, Deouell LY, Knight RT. Hierarchy of prediction errors for auditory events in human temporal and frontal cortex. Proc Natl Acad Sci U S A. 2016. Available from: 10.1073/pnas.1525030113

    Dürschmid S, Zaehle T, Hinrichs H, Heinze HJ, Voges J, Garrido MI, Dolan RJ, Knight RT. Sensory Deviancy Detection Measured Directly Within the Human Nucleus Accumbens. Cereb Cortex. 2016. Available from: 10.1093/cercor/bhu304

  • Teaching & internships

    Teaching & internships

    Stefan Dürschmid teaches students of Psychology at the OVGU in the subjects General Psychology and Biological Psychology.

    We are looking for motivated students from the subjects psychology, neuroscience, engineering, medicine and computer science with basic knowledge of a programming language (Matlab, Python etc.) for master theses or doctoral theses on the topic of stastic learning, attention fluctuation and decision making.

    We are also looking for ambitious cellists with many years of chamber music experience who are not afraid of Beethoven and Schubert.

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