My primary research interest is in the neural control of action in the most general sense. To address this issue, we use standard behavioral approaches, functional imaging in humans, and direct neural recording from areas in the frontal cortex of non-human primates.
(1) Motor learning of new dynamic environments. We have been examining the neural control of adaptation and learning in specific physical environments, usually in the form of state dependent force fields. At a conceptual level the work focuses on the learning, storage, and consolidation of and interference with internal models of motor behavior. At a translational level, the work is related to making brain mchine iterface (BMI) technology more adaptive to changing motor environments.
(2) Neural basis and mechanisms of motor learning in probabilistic environments. We examine how subjects use probabilistic information and predictability in their immediate environment to shape their actions and behavior. The learning behaviors we study range from complex probabilistic patterns to deterministic sequences.
(3) Neural control of complex spatial-temporal sequential behavior. This project examines the neural mechanisms of controlling both the spatial and the temporal components of sequences of movement. It will enable us to answer questions such as (i) Are the spatial and temporal aspects of behavior controlled independently?, and (ii) Does the brain represent abstract time and the temporal components of behavior in a similar way.
(4) The modulation of action through reward. Much of our behavior is shaped by rewards (both external and internal); we have recently begun to examine how reward (and punishment) influences motor learning. Disruption of the reward-action system may be a fundamental problem in some disease conditions such as Parkinsons disease.
(5) Decoding of force output from neural signals in frontal cortex. Current BMI technology applied to the neural control of prosthetic devices has focused almost exclusively on trajectory control. Accurate decoding of force will be necessary for the development of functinal neural prostheses. For this project, we use a number of innovative approaches to decode force from different neural signals (single cell activity, multi-unit activity, LFP and ECoG). Done in collaboration with Giuseppe Pellizzer (Neuroscience), Firat Ince (Electrical Engineering), and Ahmed Tewfik (Electrical Engineering, UT Austin).
(For a comprehensive list of recent publications, refer to PubMed, a service provided by the National Library of Medicine.)
- Fiveland E, Madhavan R, Prusik J, Linton R, Dimarzio M, Ashe J, Pilitsis J, Hancu I. EKG-based detection of deep brain stimulation in fMRI studies. Magn Reson Med. 2017 Aug 2. doi: 10.1002/mrm.26868.
- Lungu OV, Bares M, Liu T, Gomez CM, Cechova I, Ashe J. Trial-to-trial adaptation: Parsing out the roles of cerebellum and BG in predictive motor timing. J Cogn Neurosci. 2016;28(7):920-34.
- Lu X, Ashe J. Dynamic reorganization of neural activity in motor cortex during new sequence production. Eur J Neurosci. 2015;42(5):2172-2178.
- Ashe J, Bushara K. The olivo-cerebellar system as a neural clock. Adv Exp Med Biol. 2014;829:155-165.
Former Graduate Students:
Derek Dziobek (M.S. 2018, Neuroscience, University of Minnesota).
Alexandra Basford (Ph.D. 2008, Neuroscience, University of Minnesota).
Stephen Kerrigan (Ph.D. 2014, Neuroscience, University of Minnesota).