There is a fundamental gap in our understanding of how uncertainty is represented during reaching movements. Our ability to see our hand and the potential targets of our reach constantly changes: only when we foveate a well illuminated object can we know precisely where it is;in general there is broadly varying uncertainty about relative location of hand and target. The issue of how neurons encode such kinds of uncertainty for sensory and motor tasks is possibly the most active research area in computational neuroscience and a workshop on this topic organized by Lengyel and others at the 2010 Cosyne meeting was attended by more than 100 scientists. The long term goal of the proposed research is to understand how the nervous system integrates information during reaching and to use this knowledge to accelerate recovery from neuromotor diseases. The objective of this particular application is to quantify how uncertainty affects neural activities in the sensorimotor pathway. The central hypothesis is that one of the theoretically proposed models or a combination of these models will account for the neural representation of uncertainty. The rationale for the proposed research is that a better understanding of the way the nervous system represents uncertainty and promises to improve rehabilitation from neuromotor diseases because uncertainty has been shown to modulate learning speeds. Guided by preliminary data that shows our ability to perform the proposed research by pursuing three specific aims: 1) We will analyze how uncertainty about the general situation, acquired over time and called "prior" is represented. 2) We will analyze how uncertainty about the current feedback, called "likelihood" is represented. 3) We will analyze how the nervous system learns about uncertainty. The approach is innovative because it utilizes a highly integrated approach to neuroscience where advanced modeling and new data analysis is directly integrated with experiment design. The proposed research is significant, because it is expected to vertically advance our understanding of the representation of uncertainty and allows a clear distinction between competing and widely held hypotheses. Ultimately, such knowledge has the potential to inform the development of physical rehabilitation therapies. Since sensorimotor uncertainty increases as we age and with a wide range of diseases, a better understanding of the neural basis of uncertainty promises to help reduce the growing problems of an aging population.

Public Health Relevance

The proposed research is relevant to public health because an understanding of the role and representation of uncertainty is expected to lead to better interventions within rehabilitation that allow patients to recover more rapidly. Thus, the proposed research is relevant to the part of NIH's mission that pertains to developing fundamental knowledge that will help to reduce the burdens of human disability.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01NS074044-04
Application #
8653997
Study Section
Sensorimotor Integration Study Section (SMI)
Program Officer
Chen, Daofen
Project Start
Project End
Budget Start
Budget End
Support Year
4
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Rehabilitation Institute of Chicago
Department
Type
DUNS #
City
Chicago
State
IL
Country
United States
Zip Code
60611
Acuna, Daniel E; Wymbs, Nicholas F; Reynolds, Chelsea A et al. (2014) Multifaceted aspects of chunking enable robust algorithms. J Neurophysiol 112:1849-56
Kording, Konrad Paul (2014) Bayesian statistics: relevant for the brain? Curr Opin Neurobiol 25:130-3
Glaser, Joshua I; Zamft, Bradley M; Marblestone, Adam H et al. (2013) Statistical analysis of molecular signal recording. PLoS Comput Biol 9:e1003145