A central goal of neuroscience is to understand how learning is implemented by the nervous system. However, despite years of studies in animals and humans, our understanding of both the computational basis of learning and its implementation by the brain is still rudimentary. A critical gap therefore exists between the large amount of behavioral and neural data that has been collected during learning and a mathematical and biological understanding of the rules governing motor plasticity. This proposal will develop a unified mathematical theory for understanding how the brain learns complex skills. The theoretical framework will be implemented in software and will be applicable to and validated on a wide variety of sensorimotor data. The primary experimental validation system will be songbirds, which provide a physiologically accessible model system to investigate sensorimotor learning. Our objective in the songbird system is to understand sensorimotor learning of a single acoustic parameter ? fundamental frequency (pitch) ? which is known to be precisely regulated by the songbird brain. Our central hypothesis is that learning is implemented as a Bayesian inference, and that the stochastic sampling of motor commands from the current Bayesian a priori distribution of outputs is coordinated by a network of neurons in the forebrain. Drawing on a large quantity of both theoretical and experimental results, two specific aims will test this hypothesis.
The first aim will introduce an innovative new class of computational model in which the brain uses an iterative process of Bayesian inference to reshape behavior in response to sensory feedback. The models will be validated using population-averaged animal behavior.
The second aim will analyze data recorded from individual animals and single neurons in behaving animals to identify the biological mechanisms underlying sensorimotor learning. Throughout, we will design, test, and make public software that will allow other members of the community to apply our novel tools to their own data. Our approach is innovative because it will provide a unified framework for understanding the results of a wide variety of behavioral and neural studies across both tasks and species. These studies are significant because a better understanding of the mechanisms underlying sensorimotor learning could aid in the design of rehabilitative strategies that exploit the plasticity of complex behavior.

Public Health Relevance

This research will develop new mathematical methods to understand how the brain corrects errors in behavior. Such an understanding can be used improve techniques for rehabilitation in patients suffering from neurological diseases or injury.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB022872-03
Application #
9540884
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Peng, Grace
Project Start
2016-09-30
Project End
2019-06-30
Budget Start
2018-07-01
Budget End
2019-06-30
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Emory University
Department
Physics
Type
Schools of Arts and Sciences
DUNS #
066469933
City
Atlanta
State
GA
Country
United States
Zip Code
30322
Zhou, Baohua; Hofmann, David; Pinkoviezky, Itai et al. (2018) Chance, long tails, and inference in a non-Gaussian, Bayesian theory of vocal learning in songbirds. Proc Natl Acad Sci U S A 115:E8538-E8546
Sober, Samuel J; Sponberg, Simon; Nemenman, Ilya et al. (2018) Millisecond Spike Timing Codes for Motor Control. Trends Neurosci 41:644-648
Siclovan, Tiberiu M; Zhang, Rong; Cotero, Victoria et al. (2016) Fluorescence Phenomena in Nerve-Labeling Styryl-Type Dyes. J Photochem Photobiol A Chem 316:104-116
Gibbs, Summer L; Xie, Yang; Goodwill, Haley L et al. (2013) Structure-activity relationship of nerve-highlighting fluorophores. PLoS One 8:e73493
Bajaj, Anshika; LaPlante, Nicole E; Cotero, Victoria E et al. (2013) Identification of the protein target of myelin-binding ligands by immunohistochemistry and biochemical analyses. J Histochem Cytochem 61:19-30