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A headshot photo of Sergiy Yakovenko.

Sergiy Yakovenko, PhD

Associate Professor

Contact Information

PO Box 9227
108 Biomedical Road
BMRC 117
Erma Byrd Biomedical Research Building is attached to HSC North
Morgantown, WV 26506


  • Department of Human Performance - Division of Exercise Physiology
  • Department of Neuroscience
  • Rockefeller Neuroscience Institute

Graduate Training

  • University of Alberta, PhD, 2004


  • Fellow, Institute for Advanced Studies in Berlin, Germany
  • Postdoctoral Fellow, Départment de physiologie, Université de Montréal, Canada

Research Interests

I have recently established my laboratory engaged in independent neurophysiological research in motor cortex and spinal cord using new types of electrodes placed in neural structures and using new computational methods for the analysis of large neural and behavioral datasets. My team has multidisciplinary expertise from chronic long-term recording capabilities in animals with brain trauma to computational analytical skills to describe the organization principles  of movement control. With our current methods we can observe diverse neural processes over large cortical areas in the nervous system, and the analysis of this activity requires the development of parallel theoretical framework to overcome the limitations of standard reductionist methods. My strategy for the ‘reverse engineering’ of neural controller is to integrate the simplest computational models to aid in the interpretation of experimental data collected to test these same models and allow us to see the ‘big picture’. I am convinced that this framework will yield the highest degree of insight into the complex interactions within the neuro-musculo-skeletal system. 

My current research direction is focused on the principles of interactions between the mechanisms of neuromechanical hierarchy both the context of stroke and spinal cord injury using animal models and in the context of improving control of advanced arm prosthesis for human amputees. One of the challenges for the current brain-machine interface is the lack of functional understanding of how neural processes interact within and across the different levels of neuraxis. Specifically, we have limited understanding of how cortical synergies or motor primitives are controlled to produce coupled sequential activation observed in reaching movements and locomotion. Lissencephalic (smooth) rat cortex is the perfect target for the microelectrode arrays with recording-stimulation capabilities to address this question. Building on my experience in recording and stimulation of cat motor cortex and brainstem structures I have collected preliminary data in rats using floating microelectrode arrays to demonstrate the feasibility of the methods. We have developed a new type of walkway specifically designed to create a dextrous locomotor task that requires cortical contribution in rodents. In addition, we are developing neuromechanical models for data processing that will guide our analysis.

My research experience and expertise in conducting multidisciplinary studies are advantageous prerequisites to the success of proposed experimental and theoretical studies and the development of innovative technologies for rehabilitation. Results of these studies may lead to the development of novel therapies using closed-loop stimulation systems to quantify and to restore impaired motor functions.

Recent Publications


  • Yakovenko, S., Sobinov, A., Gritsenko, V. (2018) Analytical CPG model driven by limb velocity input generates accurate temporal locomotor dynamics. PeerJ. 6:e5849. PMID: 30425886
  • Sobinov, A., Yakovenko, S., Gritsenko, V., Hardesty, R., Boots, M. (2018) Approximation of complex musculoskeletal dynamics. U.S. Patent Application Serial No. 62/559,711 Filing Date September 18, 2017. (Full patent pending, submitted 2018)
  • Popov, A., Olesh, E. V., Yakovenko, S., and Gritsenko, V. (2018) A novel method of identifying motor primitives using wavelet decomposition. IEEE Xplore: 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN). DOI: 10.1109/BSN.2018.8329674. 









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