Systems Physiology of Learning

 AG Brain-Machine-Interfaces


Dr. rer. nat. Matthias Deliano

Leibniz-Institut für Neurobiologie

Brenneckestraße 6
39118 Magdeburg

Fon:         +49-391-6263-94451
Fax:         +49-391-6263-95489




In our group, we investigate the dynamics of learning and its context sensitivity and individuality by using brain-machine-interface technology. This technology allows to directly and causally interact with the brain in real-time. We focus on learning-dependent interactions with pattern states in the ongoing cortical activity through cortical electric stimulation via bidirectional electrode interfaces. Thus, we can determine momentary brain states from electrophysiological multichannel recordings, and in turn excite, suppress or modulate brain activity in a state-dependent manner through direct electric brain stimulation. By this, it is possible to investigate the neurodynamics of learning under controlled closed-loop conditions.  Assessing in parallel a broad spectrum of behavioral and physiological observables, we determine cognitive and emotional states emerging in the course of learning in a context sensitive and temporal precise way through multivariate analysis. A further aim is to obtain a detailed and causal understanding of the neural circuitry underlying the learning phenomena related to brain interfacing. This requires to establish link across systems-levels. In a translational approach, we therefore try to integrate our observations across systems levels by a comparative analysis of human and animal learning.














Fig.1: Online analysis of cortical pattern-states and state-dependent brain stimulation in rodents and humans


Rodent behavioral phyisology: shuttle-box learning, electrocorticograms, local fieldpotentials, action potentials, electric brain stimulation, current-source-density analysis, pharmacology.

Human behavioral physiology: electroencephalography, ultra-high-density electroencephalography, electromyography, electrocardiography, galvanic skin responses, transcranial electric stimulation.

Data analysis: video analysis of learning behavior, time-frequency analysis, spatial pattern analysis, real-time analysis.


Samuel Lopez Santamaria (LIN)

Dennis Pischel (LIN)


Max Happel (LIN)

Walter Freeman (University of Berkeley)

Georg Krempl (Otto-von-Guerricke Universität, Magdeburg)

Marcus Hauser (Otto-von-Guerricke Universität, Magdeburg)

Andrew Latham (University of Sidney)


LIN Special Project 2013: Fast transitions in behavior and cortical activity during associative learning in humans and rodents: a comparative approach.

Publications and Patents

Deliano M., and Ohl F.W. (2009), Neurodynamics of category learning: towards understanding the creation of meaning in the brain. New Mathematics and Natural Computation Vol. 05, issue 01, pages 61-81.

Rothe T., Deliano M., Scheich H., and Stark H. (2009), Segregation of task-relevant conditioned stimuli from background stimuli by associative learning. Brain Res. Nov 10;1297:143-59. Deliano M., Scheich H., and Ohl F.W. (2009), Auditory cortical activity after ICMS and its role for sensory processing and learning. J. Neurosci. 29(50):15898-909.

Deliano M. (2010), Prothesen für das Gehirn: Blinde sehen, Lahme gehen, Taube hören? In Böhlemann P., Hattenbach A., and Markus P. [Eds.]  Der machbare Mensch? Moderne Hirnforschung, biomedizinisches Enhancement und christliches Menschenbild (Villigst Profile 13), Lit-Verlag, Münster.

Multielektrode für CSD-Analyse, Gebrauchsmuster Nr. 10 2012 110 358.5.

Ultrahochdichte EEG-Elektrodenvorrichtung, Gebrauchsmuster Nr. 20 2012 103 726.2, IPC A61B 5/0476.