Clayton

Bingham

Ph.D.

About Me

Clayton is a postdoctoral fellow who joined Case Western Reserve University in 2019. Originally from Utah, he received his B.S. in Biology at Utah State University and M.S. in BME at University of Southern California. Clayton began his professional career as a data scientist in information services and private equity consulting. Clayton then continued his graduate training (Ph.D., 2018) at the University of Southern California, where he studied potential hippocampal prostheses for the treatment of temporal lobe disorders. In his Ph.D. dissertation he demonstrated how multi-scale models can predict how hippocampal behavior is modulated by micro-electroceutical devices. Ultimately, he was able to predict hippocampal evoked potentials under varying stimulating conditions. This effort involved integrating numerical electromagnetics, complex neural modeling, computer visualization, and parallel computing.

 

His current work focuses on understanding the level of biological realism in neural modeling required to accurately predict the therapeutic efficacy of subthalamic deep brain stimulation to treat essential tremor and Parkinson's disease.  Specifically, he aims to study the influence of axonal branching on spatiotemporal patterns of activity elicited by STN DBS. 

Neuroscience Research

 

Research Products

About Me

Clayton is a postdoctoral fellow who joined Duke University in 2021. Originally from Utah, he received his B.S. in Biology at Utah State University and M.S. in BME at University of Southern California. Clayton began his professional career as a data scientist in information services and private equity consulting. Clayton then continued his graduate training (Biomedical Engineering Ph.D., 2018) at the University of Southern California, where he studied potential hippocampal prostheses for the treatment of temporal lobe disorders. In his Ph.D. dissertation he demonstrated how multi-scale models can predict how hippocampal behavior is modulated by micro-electroceutical devices. Ultimately, he was able to predict hippocampal evoked potentials under varying stimulating conditions. This effort involved integrating numerical electromagnetics, complex neural modeling, computer visualization, and parallel computing. These innovative methods are central to the discipline of Computational Neuroscience.

 

His current work focuses on understanding the level of biological realism in neural modeling required to accurately predict the therapeutic efficacy of subthalamic deep brain stimulation to treat essential tremor and Parkinson's disease.  Specifically, he aims to study the influence of axonal branching on spatiotemporal patterns of activity elicited by STN DBS. 

 

Industry Consulting

His professional experience in academia, industry, and new ventures allows him to speak the language of engineers, domain specialists, and business leaders to encourage alignment of your firm's goals.

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