About the Project
The main goal of this project is to understand how the central nervous system (CNS) interacts with the musculoskeletal system in order to execute effective movements. These interactions remain poorly understood primarily because of the lack of unifying methodology that allows their characterization at both the behavioural and neural levels. To address this problem, the successful candidate will develop novel algorithms using techniques from machine learning, information theory and/or network theory and couple them with large-scale neurophysiological measurements of neuromuscular activity during static and dynamic motor behaviours. We will first investigate how task variables are actually translated into “muscle synergies”, i.e. how different muscles are functionally coordinated in order to perform the task at hand. Then, we will delve into the mechanisms that implement the encoding and recruitment of muscle synergies in the CNS. Finally, we will infer the neural motor commands that drive muscle activity and assess to what extent they are shared across muscles and how this is modulated by the desired motor task.
Ioannis Delis, Jacek P. Dmochowski, Paul Sajda, & Qi Wang (2018), Correlation of Neural Activity with Behavioral Kinematics Reveals Distinct Sensory Encoding and Evidence Accumulation Processes During Active Tactile Sensing. NeuroImage 175, 12-21.
Ioannis Delis, Stefano Panzeri, Thierry Pozzo & Bastien Berret (2014), A unifying model of
concurrent spatial and temporal modularity in muscle activity. Journal of Neurophysiology
Cristiano Alessandro, Ioannis Delis, Francesco Nori, Stefano Panzeri & Bastien Berret (2013),
Modularity in motor control: from input-space to task-space perspectives. Frontiers in
Computational Neuroscience 7(43).
Ioannis Delis, Bastien Berret, Thierry Pozzo & Stefano Panzeri (2013), Quantitative evaluation
of muscle synergy models: a single-trial task decoding approach. Frontiers in Computational
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