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Faculty
Faculty

Dominique Pritchett

Assistant Professor

  • Biology
  • College of Arts & Sciences

Biography

Dominique Pritchett is largely interested in understanding the neural mechanism that underlie simple associative learning behaviors.  In particular, he and his laboratory focus on the cerebellum, where much is understood about how mechanisms of synaptic plasticity contribute to the association of a stimulus with a behavioral response.  The goal of their continuing research is to understand how the cerebellar circuit contributes to associative behaviors more generally by exploring the functional connections between the cerebellum and the neocortex and the basal ganglia.

Education & Expertise

Education

B.S.
Virginia State University
2004

Ph.D.
Massachusetts Institute of Technology
2012

Expertise

Learning and Memory

Cerebellum

Optogenetics

Academics

Academics

Animal Physiology (BIOL 341)

Neurobiology (BIOL434)

Research

Research

Specialty

Learning and Memory

Group Information

Laboratory Research Detailed:Dominique Pritchett is an assistant professor of Physiology in the Department of Biology at Howard University. Prior to his current position, he obtained his PhD from the Massachusetts Institute of Technology (MIT) where he used optogentic techniques to study neural mechanisms of the selective attention. He received his post-doctoral training at the Champalimaud Neuroscience Program, in Lisbon PT, where his knowledge of optogenetics was leveraged to study the synaptic mechanisms underlying Pavlovian conditioning in the cerebellum. A central theme to the research has been on using new genetic tools to dissect neural circuits that underlie complex behaviors. Optogenetics and chemogenetics are two such tools he utilizes to gain genetic access to specific cell types in neural circuits, to alter their activity with high temporal precision. In addition, his work relies on using open-source platforms and towards developing low-cost approaches to neuroscience. His current work is focuses on understanding the neural mechanism that underlie simple associative learning behaviors. In particular, he continues to study the cerebellum, where much is understood about how mechanisms of synaptic plasticity contribute to the association of a stimulus with a behavioral response. The goal of his research is to understand how the cerebellar circuit contributes to associative behaviors more generally by exploring the functional connections between the cerebellum and the neocortex and the basal ganglia.Google Scholar: https://scholar.google.com/citations?user=NTM94R4AAAAJ&hl=enResearch Gate: https://www.researchgate.net/profile/Dominique_Pritchett

Related Articles

Locomotor activity modulates associative learning in mouse cerebellum

Albergaria C, Silva NT, Pritchett DL, Carey MR.  (2017). Locomotor activity modulates associative learning in mouse cerebellum.  Nat Neurosci. 2018 May 21(5). doi: 10.1038/s41593-018-0129-x

For things needing your attention: the role of neocortical gamma in sensory perception

Pritchett DL, Siegle JH, Deister CA, Moore CI. (2015). For things needing your attention: the role of neocortical gamma in sensory perception.  Curr. Opin Neurobiol. 31, 254 - 263.

Attention drives synchronization of alpha and beta rhythms between right inferior frontal and primary sensory neocortex

Sacchet MD, LaPlante RA, Wan Q, Pritchett DL, Lee AK, Hämäläinen M, Moore CI, Kerr CE, Jones SR.  (2015). Attention drives synchronization of alpha and beta rhythms between right inferior frontal and primary sensory neocortex.  J Neurosci. 35(5), 2074-2082.

Gamma-range synchronization of fast-spiking interneurons can enhance detection of tactile stimuli

Siegle JH*, Pritchett DL*, Moore CI.  (2014). Gamma-range synchronization of fast-spiking interneurons can enhance detection of tactile stimuli. Nat Neurosci. 17(10), 1371-1379.

A matter of trial and error for motor learning

Pritchett DL, Carey MR. (2014). A matter of trial and error for motor learning. Trends Neurosci. 37(9), 465-466.

The flexDrive: an ultra-light implant for optical control and highly parallel chronic recording of neuronal ensembles

Voigts J, Siegle JH, Pritchett DL, Moore CI. (2013). The flexDrive: an ultra-light implant for optical control and highly parallel chronic recording of neuronal ensembles in freely moving mice. Frontiers in System Neuroscience. 13, 7-8.

Dynamics of dynamics within a single data acquisition session: variation in neocortical alpha oscillations in human MEG.

Wan Q, Kerr C, Pritchett D, Hämäläinen M, Moore C, Jones S. (2011). Dynamics of dynamics within a single data acquisition session: variation in neocortical alpha oscillations in human MEG.  Plos One. 6(9), e24941. 

Effects of mindfulness meditation training on anticipatory alpha modulation in primary somatosensory cortex

Kerr CE, Jones SR, Wan Q, Pritchett DL, Wasserman RH, Wexler A, Villanueva JJ, Shaw JR, Lazar SW, Kaptchuk TJ, Littenberg R, Hämäläinen MS, Moore CI. (2011). Effects of mindfulness meditation training on anticipatory alpha modulation in primary somatosensory cortex. Brain Res Bull. 85(3-4), 96-103.

Local dynamic gain modulation drives enhanced efficacy and efficiency of signal transmission.

Knoblich U, Siegle JH, Pritchett DL, Moore CI. (2011). What do we gain from gamma? Local dynamic gain modulation drives enhanced efficacy and efficiency of signal transmission. Front. Hum. Neurosci. 4, 185.

Cued spatial attention drives functionally relevant modulation of the mu rhythm in primary somatosensory cortex

Jones SR, Kerr CE, Wan Q, Pritchett DL, Hämäläinen M, Moore CI. (2010). Cued spatial attention drives functionally relevant modulation of the mu rhythm in primary somatosensory cortex. J. Neurosci. 30(41) 13760 - 13765.

Transformations in oscillatory activity and evoked responses in primary somatosensory

Ziegler DA, Pritchett DL, Hosseini-Varnamkhasti P, Corkin S, Hämäläinen M, Moore CI, Jones SR. (2010). Transformations in oscillatory activity and evoked responses in primary somatosensory cortex in middle age: a combined computational neural modeling and MEG study. Neuroimage. 52(3) 897 - 912.

Quantitative analysis and biophysically realistic neural modeling of the MEG mu rhythm

Jones SR, Pritchett DL, Sikora MA, Stufflebeam SM, Hämäläinen M, Moore CI. (2009). Quantitative analysis and biophysically realistic neural modeling of the MEG mu rhythm:  rhythmogenesis and modulation of sensory-evoked responses. J. Neurophysiol. 102(6), 3554-3572. 

Neural correlates of tactile detection: a combined magnetoencephalography and biophysically

Jones SR, Pritchett DL, Stufflebeam SM, Hämäläinen M, Moore CI. (2007). Neural correlates of tactile detection: a combined magnetoencephalography and biophysically based computational modeling study. J. Neurosci. 27(40), 10751 - 10764.