​We currently have multiple openings for Postdocs and/or engineers for two newly funded studies. Detailed descriptions of each study are listed below. If interested, please send a CV to Dr. Dawn Taylor at dxt42@case.edu.

1) The goal of the Parkinson's project is to understand how Parkinson's disease alters cortical network processing and how Deep Brain Stimulation (DBS) can be improved to better re-normalize motor processing. Cortical network activity will be recorded in monkeys via intracortical electrode arrays before and after making the animals hemi-Parkinsonian via injections of the selective neurotoxin MPTP (note this toxin is initially applied in such a way that the animal only develops parkinsonian symptoms on one half of its body so the animal can still groom, ambulate, and take care of itself). DBS will then be applied to two subcortical regions (subthalamic nucleus and globus pallidus) to alleviate Parkinsonian symptoms. Computational models of the cortical microcircuit will be developed that accurately reflect the recorded cortical activity in the normal and Parkinsonian states as well as under the influence of different DBS patterns. The model parameters that need to be changed to make the network computational model behave like the recorded experimental data under the different conditions will be informative regarding the underlying mechanisms of DBS. The refined computational model will also be used to rapidly screen for new DBS patterns that are more effective at restoring normal cortical network processing. The improved DBS patterns suggested by the computational model will then be validated by testing those novel patterns in the monkeys. This project is being done in conjunction with Dr. Cameron McIntyre who will oversee the computational modelling part of the study. We are looking for multiple post docs that can focus on either the animal work, the computational modeling, or both depending on their skills and interests.

2) The long-term goal of the Neuroprosthetics project is to enable paralyzed individuals to use their brain signals to control their upper limb via implanted muscle stimulators.  Most labs working on brain-controlled neuroprosthetics decode intended limb kinematics (e.g. velocity, joint angles, etc.) from the recorded brain signals. However, that approach still requires converting those kinematic commands into the appropriate stimulation patterns required to generate the desired limb motion. That conversion process has not been resolved for the upper limb due to the limb's complex dynamical nature and the fact that the limb is subject to unknown external forces during use. We bypass this obstacle by retraining the brain to control muscle stimulators directly. We have come up with some novel, but clinically feasible ways of mapping neural signals directly to muscle stimulators. Our methods can enable the user to have good control over both limb motion and stiffness. To demonstrate and refine our methods, we are training monkeys to control the movements of a realistic musculoskeletal model of a paralyzed limb activated via implanted muscle stimulators. The paralyzed limb simulator (developed by the lab of Robert Krisch) provides real-time visual feedback to the animal of the limb motion that would result from stimulating the paralyzed muscles based on the animal's neural signals decoded in real time. The use of this real-time paralyzed arm simulator allows us to test and refine our process of brain-controlled muscle stimulation in monkeys without actually having to paralyze any animals.