Learning Hierarchical Models for Motor Control

Learning Hierarchical Models for Motor Control

Research Area:

Clinical Movement Control and Rehabilitation

Researchers:

Alessandro Salatiello; Martin A. Giese;

Collaborators:

Neville Hogan; Tamar Flash; Dagmar Sternad

Proposed start date:

2018-05-01

Proposed end date:

2021-08-02
  

Description:

There is strong evidence that the animal Motor Control System is hierarchically organized into highly-interacting specialized subnetworks. Such distributed control architecture guarantees remarkable robustness, flexibility and dexterity, which roboticists have been striving to achieve.

Unsurprisingly, the identification of such architecture is a complex problem, which requires a joint effort of experimental and theoretical neuroscientists. A typical scenario in this research area is the one in which electrophysiologists collect data at only one level of the hierarchy (e.g. Primary Motor Cortex or Neuromuscular Interface) and they would like to uncover the underlying structure that generated the recorded pattern of activation. In our lab, we develop algorithms that help to identify such hierarchical structures automatically from available neural and behavioral data.

As one approach, we combine methods from System Identification Theory (e.g. Unscented Kalman Smoother) and Machine Learning (e.g. Dynamical Neural Networks and Structure Learning) to automatically identify the hierarchy of neural networks underlying the generation of movements, e.g. in the spinal chord. Present work focuses on finding robust algorithms and the analysis of the uniqueness of the solutions of the underlying system identification problem.

 

Funding: CRCNS US-German-Israeli Collaborative Research Proposal: Hierarchical Coordination of Complex Actions, funded by the BMBF (FKZ: 01GQ1704).

Publications

Salatiello, A. & Giese, M. A. (2020). Recurrent Neural Network Learning of Performance and Intrinsic Population Dynamics from Sparse Neural Data. Artificial Neural Networks and Machine Learning – ICANN 2020 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I. Springer, Berlin(874-886). [More] 
Salatiello, A. & Giese, M. A (2020). Recurrent Neural Network Learning of Performance and Intrinsic Population Dynamics from Sparse Neural Data. ICANN2020, arXiv:2005.02211v1 [q-bio.NC] . [More] 
Salatiello, A. & Giese, M. A (2019). Learning of Generative Neural Network Models for EMG Data Constrained by Cortical Activation Dynamics (A). 29th Meeting of the Society for the Neural Control of Movement; Toyama, Japan . [More] 
Salatiello, A. & Giese, M. A (2019). Learning of generative neural network models for EMG data constrained by cortical activation dynamics(B). CNS Conference 2019, 13-17 July, Barcelona, Spain . [More] 
Salatiello, A. & Giese, M. A (2019). Learning Central Pattern Generator models for rhythmic activation patterns. In Proceedings : Göttingen Meeting of the German Neuroscience Society 2019, Germany, T23-10C . [More]