Werner Reichardt Centrum für Integrative Neurowissenschaften (CIN)

Computational Sensomotorics

The Section for Computational Sensomotorics investigates theoretical principles in the perception and control of motor actions. Research is highly interdisciplinary, including psychophysical and clinical experimentation, the development of mathematical and computational models based on pysiology, and the development of technical systems that exploit brain-inspired principles or support accurate diagnosis and rehabilitation training in neurological diseases.

Research Areas

Clinical Movement Control and Rehabilitation

Applying advanced computational methods, we analyze the body movements and behavior of patients with neurological and psychiatric disorders. Goals of this work are to identify and to quantify disorder-specific or lesion-specific changes in motor behaviour, including especially complex whole-body movements like gait or interactive tasks. Our work addresses movement deficits associated with various neurological disorders, including cerebellar ataxia, Parkinson's disease and apraxia. Another focus of this work is the investigation of motor adaptation and training effects in normal participants and during motor rehabilitation training for neurological patients.

Neural and Computational Principles of Action and Social Processing

We investigate the neural mechanisms of the perception of body movements, and their relationship with motor execution and social signals. Our work combines psychophysical experiments and the development of physiologically-inspired neural models in close collaboration with electrophysiologists at the HIH and the CIN. In addition, exploiting advanced methods from computer animation and Virtual Reality (VR), we investigate the perception of body movements (facial and body expressions) in social communication, and its deficits in psychiatric disorders, such as schizophrenia or autism spectrum disorders.

Biomedical and Biologically-Motivated Technical Applications

Brains control and recognize body and facial movements better than any existing technical system. We study the computational principles underlying recognition and motor control of body movements in biological systems and transfer relevant principles to technical applications. Application domains include computer graphics, computer vision, and humanoid robotics. In these fields, the modeling of movements of humans and their  interactive bebehavior becomes increasingly important. In addition, we exploit such technical systems for movement synthesis and recognition in the context of biomedical applications, such as rehabilitation training.

Selected Publications

  • Fedorov, L., Chang, D., Giese, M. A., Bülthoff, H. & de la Rosa, S. (2018). Adaptation aftereffects reveal representations for encoding of contingent social actions. PNAS, 115(29), 7515-7520.
  • Li, B., Virtanen, J. P., Oeltermann, A., Schwarz, C., Giese, M. A., Ziemann, U. et al. (2017). Lifting the Veil on the Dynamics of Neuronal Activities Evoked by Transcranial Magnetic Stimulation. eLife pii: e30552.
  • Giese, M. A. (2016) Face Recognition: Canonical Mechanisms at Multiple Timescales. Curr Biol.26(13), 534-537.
  • Giese, M. A. & Rizzolatti, G. (2015). Neural and Computational Mechanisms of Action Processing: Interaction between Visual and Motor Representations. Neuron, 88(1), 167-180.
  • Giese, M. A. (2014). Mirror representations innate versus determined by experience: A viewpointfrom learning theory. Behavioural and Brain Sciences, 37(2), 201-202.
  • Caggiano, V., Fogassi, L., Rizzolatti, G., Casile, A., Giese, M. A. & Thier, P. (2012). Mirror neuronsencode the subjective value of an observed action. PNAS, 109(29), 11848-11853.  
  • Roether, C. L., Omlor, L. & Giese, M. A. (2008). Lateral asymmetry of bodily emotion expression. Current  Biology, 18, R329-330.
  • Leopold, D. A., Bondar, I. V. & Giese, M. A. (2006). Norm-based face encoding by single neurons in the monkey inferotemporal cortex. Nature, 442, 572-575.
  • Giese, M. A. & Poggio, T. A. (2003). Neural mechanisms for the recognition of biological movements and action. Nature Reviews Neuroscience, 4, 179-192.