9 May 2011, h.15:00 - Sala 301 DEI/A
University Clinic Tübingen, Tübingen, Germany
An important question in neuroscience is
understanding how the central nervous system (CNS) controls the
large number of degrees-of-freedom of the musculoskeletal
apparatus to perform a wide repertoire of complex motor behaviors.
A long standing hypothesis is that the CNS relies on a modular
architecture to simplify motor control and motor learning.
I will first present in this talk the results of a series of experiments that I have carried out with human subjects to study the low-dimensional structures underlining the kinematics and electromyographic (EMG) activity recorded from a large set of body muscles during the execution of complex whole-body movements. These results link together, in a hierarchical view of motor control, the joint coordination characterizing whole-body pointing movements with a basic muscle synergistic organization, namely a triphasic pattern.
I will also describe the unsupervised learning algorithms that I used to analyze different biological data, and I will describe how the imposition of specific constrains and priors to the algorithms can lead to well-defined physiological interpretations.
At the end I will present partial results of the work I’m currently carrying on in the framework of an important EU project, AMARSi (Adaptive Modular Architectures for Rich Motor Skills). In a series of experiments in a virtual reality environment we are collecting kinematic data from human subjects during the accomplishments of ad-hoc walking plus pointing motor tasks. Such data are being used, in a theoretical control framework, to extract periodic and non-periodic synergies to generate and control movements of characters in graphical applications and robotics.
Section for Computational Sensomotorics, Department of Cognitive Neurology, Hertie Institute for Clinical Brain Research, Centre for Integrative Neuroscience, University Clinic Tübingen, Tübingen, Germany