Valentin Dragoi received his bachelor’s degree in computer engineering from the Technical University of Iasi (Romania) in 1989. He subsequently received his Ph.D. degree from Duke University in 1997.  His thesis was focused on learning and adaptive behavior and on computational neuroscience. As a postdoctoral fellow in Dr. Mriganka Sur’s laboratory in the Department of Brain and Cognitive Sciences and the Picower Center for Learning and Memory at MIT, Valentin investigated the neural circuits of cortical adaptation in behaving animals. In 2003, he became an Assistant Professor at the McGovern Medical School at Univ. of Texas, Houston, where he currently holds the Levit Distinguished Professorship in Neuroscience. Valentin has been awarded a string of awards including the STARs Award from the UT System (2019), NIH BRAIN Awards (2015-present), NIH Director’s Pioneer Award (2010), NIH EUREKA Award (2009), James S. McDonnell Award (2005), Pew Scholars Award (2004), etc.

Areas of Interest

Research Interests

Cortical Circuits for Information Processing and Decision Making

Despite major advances in understanding the properties of single cells and molecule-level processes, how the cerebral cortex operates at the circuit level continues to remain mysterious. For the past several decades, neuroscientists have observed remarkable regularity in the neural architecture: cortical areas communicate through feedforward, lateral, and feedback connections. Clearly, understanding the functional principles of cortical communication is key to understanding how the entire cortex operates. My laboratory has embarked on a quest to understand the principles behind the network encoding of sensory information and executive control in cerebral cortex. Our long-range goal is to understand the mechanisms underlying state and experience-dependent changes in the function of cortical populations and how the coordination of distributed networks of neurons influences behavior. To accomplish these goals, we combine electrophysiological (multi-electrode recording in restrained and freely moving non-human primates), optogenetic and electrical stimulation, behavioral approaches, and computational methods. Our basic strategy is to help develop new tools for modulating and recording population activity across cortical circuits in restrained and unrestrained animals and then apply these techniques to examine the neural computations and coding principles across cortical circuits.

There are three major research directions in my lab:

  • State and experience-dependent changes in cortical networks underlying behavioral decisions. One of our long-standing interests is to understand the relationship between the activity of populations of cortical cells and behavioral decisions. Our working hypothesis is that the accuracy of sensory representation and intracortical communication determines the accuracy of behavioral responses.
  • Optogenetic manipulation of cortical circuits. Viral tools for gene delivery have allowed new optogenetic methods to target cells based on cell localization and connectivity. Physiological dissection of targeted circuits, primarily by depolarizing or hyperpolarizing rhodopsins, has been extremely successful in the mouse brain, but remain of limited use in non-human primate (NHP) and human brain. We have just started s series of projects to test the function of cortical circuits by manipulating their responses and then examine the impact on perceptual decision making.
  • Real-time network interactions underlying complex behavior in freely moving animals. It has become increasingly understood that studying the brain in a restrained laboratory rig poses severe limits on our capacity to understand the function of brain circuits. To overcome these limitations, we have constructed a wireless system that allows us to study cortical dynamics at the population level while nonhuman primates are moving freely in their natural environment. Phenomena that were difficult or impossible to observe in an experimental rig, such as foraging, sleep, or social behavior are now possible to study.


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  • Andrei AR, Pojoga S, Janz R, and Dragoi V. (2019) Integration of cortical population signals for visual perception, Nature Communications, Aug 23;10(1):3832. doi: 10.1038/s41467-019-11736-2.

  • Nigam, S, Pojoga, S, and Dragoi V. (2019) Synergistic coding of visual information in columnar networks, Neuron, Oct 23; 104(2):402-411.e4. doi: 10.1016/j.neuron.2019.07.006.

  • Shahidi, N, Andrei, A, Hu, M, and Dragoi V. (2019) High order coordination of cortical spiking activity modulates perceptual accuracy. Nature Neuroscience, Jul;22(7):1148-1158 2019, doi: 10.1038/s41593-019-0406-3.

  • Gutnisky D, Beaman, C, Lew, S., and Dragoi V. (2017) Cortical states for improved sensory discrimination. eLife Dec 23;6. pii: e29226. doi: 10.7554/eLife.29226.

  • Beaman, C., Eagleman SL, and Dragoi V. (2017) Sensory coding accuracy and perceptual performance are improved during the desynchronized cortical state. Nature Communications 2017 Nov 3;8(1):1308. doi: 10.1038/s41467-017-01030-4

  • Wang Y and Dragoi V. (2015) Rapid learning in visual cortical networks. eLife, doi: 10.7554/eLife.08417  [Epub ahead of print]

  • Hansen BJ, Chelaru MI, and Dragoi V. (2012) Correlated variability in laminar cortical circuits, Neuron 76, 590-602.

  • Eagleman S, and Dragoi V. (2012) Awake replay of image sequences in visual cortical networks. Proc. Natl. Acad. Sci. USA, 109: 19450-5.

  • Hansen BJ and Dragoi V. (2011). Laminar-specific adaptive synchronization in visual cortex. Proc. Natl. Acad. Sci. USA, 108:10720-10725.

  • Gutnisky D, Hansen B, Iliescu B, and Dragoi V. (2009). Attention limits plasticity of visual processing during exposure-based learning. Current Biology, 19, 555-560.

  • Chelaru M. I. and Dragoi V. (2008). Efficient coding in heterogeneous neuronal populations. Proc. Natl. Acad. Sci. USA, 105, 16344-16349.

  • Gutnisky D and Dragoi V. (2008). Adaptive coding of visual information in neural populations. Nature, 452, 220-224.