Jeffrey Beck

Jeffrey Beck

Assistant Professor of Neurobiology

We study neural coding and computation from a theoretical perspective with particular emphasis on probabilistic reasoning and decision making under uncertainty, complex behavioral modeling, computational models of cortical circuits and circuit function, dynamics of spiking neural networks, and statistical analysis of neural and behavioral data.  Previous work has been largely concerned with sensory-motor transformations and neural representations of complex stimuli such as odors.  More recently, we have been focusing on developing non-linear latent state space models of neural networks as standard linear models are incapable of generating even very simple behaviors.  

Appointments and Affiliations

  • Assistant Professor of Neurobiology
  • Assistant Professor of Electrical and Computer Engineering
  • Assistant Professor of Biomedical Engineering
  • Investigator in the Duke Institute for Brain Sciences

Contact Information

  • Email Address: jeff.beck@duke.edu

Education

  • Ph.D. Northwestern University, 2003

Research Interests

Jeffrey Beck studies neural coding and computation from a theoretical perspective, with particular emphasis on probabilistic reasoning and decision making under uncertainty, complex behavioral modeling, computational models of cortical circuits and circuit function, dynamics of spiking neural networks, and statistical analysis of neural and behavioral data.

Courses Taught

  • NEUROSCI 755: Interdisciplinary Program in Cognitive Neuroscience (IPCN) Independent Research Rotation
  • STA 993: Independent Study

Representative Publications

  • Oh, H; Beck, JM; Zhu, P; Sommer, MA; Ferrari, S; Egner, T, Satisficing in split-second decision making is characterized by strategic cue discounting, Journal of Experimental Psychology: Learning, Memory, and Cognition, vol in press (2016) [abs].
  • Dowd, EW; Kiyonaga, A; Beck, JM; Egner, T, Quality and accessibility of visual working memory during cognitive control of attentional guidance: A Bayesian model comparison approach, Visual Cognition, vol 23 no. 3 (2015), pp. 337-356 [10.1080/13506285.2014.1003631] [abs].
  • Jiang, J; Beck, J; Heller, K; Egner, T, An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands., Nature Communications, vol 6 (2015) [abs].
  • Dowd, EW; Kiyonaga, A; Beck, JM; Egner, T, Probability of guessing, not precision, changes in mixture models of visual working memory during cognitive control of attentional guidance, Visual Cognition, vol 22 no. 8 (2014), pp. 1027-1030 [10.1080/13506285.2014.960669] [abs].
  • Moreno-Bote, R; Beck, J; Kanitscheider, I; Pitkow, X; Latham, P; Pouget, A, Information-limiting correlations, Nature Neuroscience, vol 17 no. 10 (2014), pp. 1410-1417 [10.1038/nn.3807] [abs].