Assessing the effects of cocaine dependence and pathological gambling using group-wise sparse representation of natural stimulus FMRI data.

Assessing functional brain activation patterns in neuropsychiatric disorders such as cocaine dependence (CD) or pathological gambling (PG) under naturalistic stimuli has received rising interest in recent years. In this paper, we propose and apply a novel group-wise sparse representation framework to assess differences in neural responses to naturalistic stimuli across multiple groups of participants (healthy control, cocaine dependence, pathological gambling). Specifically, natural stimulus fMRI (N-fMRI) signals from all three groups of subjects are aggregated into a big data matrix, which is then decomposed into a common signal basis dictionary and associated weight coefficient matrices via an effective online dictionary learning and sparse coding method.

Motor learning reduces eye movement variability through reweighting of sensory inputs.

Motor learning can improve both the accuracy and precision of motor performance. We analyzed changes in the average trajectory and the variability of smooth eye movements during motor learning in rhesus monkeys. Training with a compound visual-vestibular stimulus could reduce the variability of the eye movement responses without altering the average responses.

Elimination of climbing fiber instructive signals during motor learning.

The climbing fiber input to the cerebellum from the inferior olive is thought to act as a teacher whose activity controls the induction of motor learning. We designed training conditions that did not elicit instructive signals in the climbing fibers, but nevertheless induced robust and consistent motor learning in the vestibulo-ocular reflex of rhesus monkeys. Our results indicate that instructive signals in the climbing fibers are not necessary for cerebellum-dependent learning.