Neural Systems Engineering and Information Processing
Postdoctoral Position Available
We're recruiting postdoctoral scholars with control and signal processing background to develop closed-loop control algorithms for neural systems. Applicants with prior experience in stochastic control are especially encouraged to apply. The project involves both theoretical development and algorithm design, and validation using human neural data. The developed system will eventually be implemented in real-time control experiments with human recordings. Interested candidates should apply by sending their CV to firstname.lastname@example.org.
Our laboratory develops algorithmic solutions to problems in basic and clinical neuroscience that involve the collection and manipulation of neural signals. Our work combines algorithm development with in vivo experimental implementation and testing, and is conducted in close collaboration with a variety of experimental labs.
In our research we use the principles of information and control theories, statistical inference, and signal processing to gain insight into basic neuroscience questions and to develop effective solutions for clinical neuroscience problems and non-clinical applications.
One problem of particular interest to our lab is the design of real-time brain-machine interface (BMI) architectures. This includes the development of BMIs that aim to restore original motor function in disabled patients, and BMIs that have the potential to also enhance such original motor function. These BMIs record the neural activity in the relevant brain areas and use diverse mathematical tools to infer from this activity the motor intent of the user. We also work on developing BMIs for automatic control of anesthesia based on non-invasive neural recordings.
- Shanechi M. M., Hu R., Williams, Z.M., "A cortical-spinal prosthesis for targeted limb movement in paralyzed primate avatars", Nature Communications, 5:3237, Feb. 2014 (pdf) (link)
- Shanechi M. M., Chemali J., Liberman M., Solt K., Brown E. N. "A brain-machine interface for control of medically-induced coma", PLOS Computational Biology, 9(10), Oct. 2013 (pdf) (link)
- Shanechi M. M., Hu R., Powers M., Wornell G. W., Brown E. N., Williams Z. M. "Neural population partitioning and a concurrent brain-machine interface for sequential motor function", Nature Neuroscience, 15 (12), Dec. 2012. (pdf) (link)
- Shanechi M. M., Wornell G. W., Williams Z. M., Brown E. N. "Feedback-controlled parallel point process filter for estimation of goal-directed movements from neural signals", IEEE Trans. on Neural Syst. Rehabil. Eng., Oct. 2012. (pdf) (link)
- Shanechi M. M., Williams Z. M., Wornell G. W., Hu R. C., Powers M., Brown E. N. "A real-time brain-machine interface combining motor target and trajectory intent using an optimal feedback control design", PLOS ONE 8 (4), Apr. 2013 (pdf) (link)
- Shanechi M. M., Hu R., Powers M., Wornell G. W., Brown E. N., Williams Z. M. "A brain-machine interface combining target and trajectory information using optimal feedback control", in Computational and Systems Neuroscience (COSYNE) Meeting (Salt Lake City, USA, 2011).