Neurometric Modeling: Computational Modeling of Individual Brains
Abstract
Warfighters have benefited significantly from the enormous advances in digital technology over the past several decades. In contrast, too little of the considerable progress in neuroscience has been applied to improving warfighter performance. We believe this reflects the absence of digital technology that can help bridge the gap between neuroscience and digital systems. We believe this gap might be filled by constructing a computational model of the neuro-cognitive activity of the warfighter. We propose that such a model could be created by algorithms applied to measurements of brain activity obtained using functional MRI. Algorithmic processing of these measurements can exploit a variety of statistical machine learning methods to synthesize a new kind of neuro-cognitive model, which we call neurometric models. These executable models could be incorporated into a number of applications for assessing and improving mental performance, including battlefield training and treatment of disorders such as PTSD. The long term goal is to enable systems that can better adapt to the warfighter in real-time due to model-generated hypotheses about the individual's neuro-cognitive state.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 16, 2011
- Accession Number
- ADA547370
Entities
People
- Bruce Naylor
- David Ress
- Risto Miikkulainen
Organizations
- University of Texas at Austin