Core B: Bayesian and decision theoretic tools The production of movement sequences is inherently affected by uncertainty: to move rapidly the animal needs to estimate what to do next given past knowledge. Such estimates can never be certain. A colorful example from a recently popular book (Taleb, 2008) shows that we can never be certain about a sequence of events. The turkey that has been fed every day for close to a year gets slaughtered for Thanksgiving. Many communities such as robotics, economics, data mining and models of human behavior are converging on a common approach towards formalizing uncertainty: Bayesian decision theory. We will first use these methods to predict behaviors from each of the three experimental labs. We will continue to extract the relevant variables (timescales, probabilities) that need to be represented by the nervous system to efficiently produce sequences. These variables will then be correlated with measured neural signals to ask how these variables are represented. Moreover, uncertainty is central when analyzing data from neurons. When we are asking how neurons store and recall motor sequences we never directly measure the relevant variables, such as niemory, we rather measure spikes or imaging signals that are affected by noise. A central topic for neural data analysis, therefore, is to combine many measurements (say 1000 spikes) into an estimate (of say tuning properties) that has small uncertainty (or narrow error-bars). We will use state of the art Bayesian data analysis techniques to analyze the data resulting from the proposed experiments in the other projects. Specifically we are interested in asking how neurons interact with one another using these Bayesian methods. Lastly, we will use state of the art decoding methods to ask how well various types of information are encoded by the measured signals. This is useful for the experimental projects as it allows asking how much information about a, variable of interest is encoded by neural signals.

Public Health Relevance

The proposed work is central to the problem of understanding the mechansims where practice leads to to reorganizafion of the human motor system in the face of aging, neurodenerafion, stroke or brain injury. Understanding these mechansims has an impact on the design of therapies directed at preserving function, developing compensator movements and ulfimately, developing novel motor capacity.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Program Projects (P01)
Project #
5P01NS044393-09
Application #
8380915
Study Section
National Institute of Neurological Disorders and Stroke Initial Review Group (NSD)
Project Start
Project End
Budget Start
2012-09-01
Budget End
2013-08-31
Support Year
9
Fiscal Year
2012
Total Cost
$67,410
Indirect Cost
$8,197
Name
University of California Santa Barbara
Department
Type
DUNS #
094878394
City
Santa Barbara
State
CA
Country
United States
Zip Code
93106
Bogdanov, Petko; Dereli, Nazli; Dang, Xuan-Hong et al. (2017) Learning about learning: Mining human brain sub-network biomarkers from fMRI data. PLoS One 12:e0184344
Huang, Weiyu; Goldsberry, Leah; Wymbs, Nicholas F et al. (2016) Graph Frequency Analysis of Brain Signals. IEEE J Sel Top Signal Process 10:1189-1203
Ohbayashi, Machiko; Picard, Nathalie; Strick, Peter L (2016) Inactivation of the Dorsal Premotor Area Disrupts Internally Generated, But Not Visually Guided, Sequential Movements. J Neurosci 36:1971-6
Crossley, Matthew J; Horvitz, Jon C; Balsam, Peter D et al. (2016) Expanding the role of striatal cholinergic interneurons and the midbrain dopamine system in appetitive instrumental conditioning. J Neurophysiol 115:240-54
Ramkumar, Pavan; Acuna, Daniel E; Berniker, Max et al. (2016) Chunking as the result of an efficiency computation trade-off. Nat Commun 7:12176
Wymbs, Nicholas F; Grafton, Scott T (2015) The Human Motor System Supports Sequence-Specific Representations over Multiple Training-Dependent Timescales. Cereb Cortex 25:4213-25
Bassett, Danielle S; Yang, Muzhi; Wymbs, Nicholas F et al. (2015) Learning-induced autonomy of sensorimotor systems. Nat Neurosci 18:744-51
Gu, Shi; Pasqualetti, Fabio; Cieslak, Matthew et al. (2015) Controllability of structural brain networks. Nat Commun 6:8414
Acuna, Daniel E; Berniker, Max; Fernandes, Hugo L et al. (2015) Using psychophysics to ask if the brain samples or maximizes. J Vis 15:
Cieslak, Matthew; Ingham, Roger J; Ingham, Janis C et al. (2015) Anomalous white matter morphology in adults who stutter. J Speech Lang Hear Res 58:268-77

Showing the most recent 10 out of 115 publications