David Heeger

About

David Heeger is the Silver Professor of Psychology and Neural Science at New York University, where he is also a faculty member at the Center for Neural Science. He holds a B.A. in Mathematics and a Ph.D. in Computer Science from the University of Pennsylvania, completed in 1983 and 1987 respectively. His postdoctoral work was conducted at the MIT Media Lab from 1987 to 1990, followed by a research scientist position at NASA Ames Research Center. Heeger has held academic positions at Stanford University, serving as an Assistant Professor from 1991 to 1998 and an Associate Professor from 1998 to 2002. His research focuses on computational neuroimaging and perception, with over 130 journal papers and six US patents to his name. He has received numerous awards, including the David Marr Prize in computer vision, the Alfred P. Sloan Research Fellowship, the Troland Award in psychology from the National Academy of Sciences, and the Margaret and Herman Sokol Faculty Award in the Sciences at NYU. In 2013, he was elected a member of the National Academy of Sciences. His work encompasses a broad range of topics in perception and neural systems, and he is actively involved in teaching undergraduate and graduate courses in perception, computational neuroscience, and neural imaging.

Research topics

• Artificial Intelligence
• Computer Science
• Neuroscience
• Cognitive psychology
• Biology
• Psychology
• Mathematics
• Endocrinology

Selected publications

• Differential impact of endogenous and exogenous attention on activity in human visual cortex

  Scientific Reports · 2020 · 102 citations

  How do endogenous (voluntary) and exogenous (involuntary) attention modulate activity in visual cortex? Using ROI-based fMRI analysis, we measured fMRI activity for valid and invalid trials (target at cued/un-cued location, respectively), pre- or post-cueing endogenous or exogenous attention, while participants performed the same orientation discrimination task. We found stronger modulation in contralateral than ipsilateral visual regions, and higher activity in valid- than invalid-trials. For e…

  DOI

• A dynamic normalization model of temporal attention

  Nature Human Behaviour · 2021 · 78 citations

  Senior authorCorresponding
  DOI

• A recurrent circuit implements normalization, simulating the dynamics of V1 activity

  Proceedings of the National Academy of Sciences · 2020 · 73 citations

  1st authorCorresponding

  The normalization model has been applied to explain neural activity in diverse neural systems including primary visual cortex (V1). The model's defining characteristic is that the response of each neuron is divided by a factor that includes a weighted sum of activity of a pool of neurons. Despite the success of the normalization model, there are three unresolved issues. 1) Experimental evidence supports the hypothesis that normalization in V1 operates via recurrent amplification, i.e., amplifyin…

  DOI

• Traveling waves in the human visual cortex: An MEG-EEG model-based approach

  PLoS Computational Biology · 2025-04-17 · 13 citations

  articleOpen access

  Brain oscillations might be traveling waves propagating in cortex. Studying their propagation within single cortical areas has mostly been restricted to invasive measurements. Their investigation in healthy humans, however, requires non-invasive recordings, such as MEG or EEG. Identifying traveling waves with these techniques is challenging because source summation, volume conduction, and low signal-to-noise ratios make it difficult to localize cortical activity from sensor responses. The diffic…

  PublisherDOI

• Anticipatory and evoked visual cortical dynamics of voluntary temporal attention

  Nature Communications · 2024-10-21 · 12 citations

  articleOpen access

  We can often anticipate the precise moment when a stimulus will be relevant for our behavioral goals. Voluntary temporal attention, the prioritization of sensory information at task-relevant time points, enhances visual perception. However, the neural mechanisms of voluntary temporal attention have not been isolated from those of temporal expectation, which reflects timing predictability rather than relevance. Here we use time-resolved steady-state visual evoked responses (SSVER) to investigate…

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01MH069880

  NIH · $1.4M · 2011

• The neural representation and transformation of color in human visual cortex

  NIH · $1.1M · 2012–2016

• NIH Grant R21DA024423

  NIH · $1.1M · 2013

Frequent coauthors

• Elisha P. Merriam

  35 shared

• Marisa Carrasco

  New York University

  34 shared

• Marlene Behrmann

  Carnegie Mellon University

  23 shared

• Laura Dugué

  Centre de recherche cerveau et cognition

  22 shared

• Michael S. Landy

  New York University

  21 shared

• Geoffrey M. Boynton

  University of Washington

  20 shared

• Ilan Dinstein

  Center for Autism and Related Disorders

  16 shared

• Tobias H. Donner

  University Medical Center Hamburg-Eppendorf

  16 shared

Labs

• Computational Neuroimaging LaboratoryPI

Education

• B.A., Mathematics

  University of Pennsylvania

  1983

• Ph.D., Computer Science

  University of Pennsylvania

  1987

Awards & honors

• David Marr Prize in computer vision (1987)
• Alfred P. Sloan Research Fellowship (1994)
• Troland Award in psychology from the National Academy of Sci…
• Margaret and Herman Sokol Faculty Award in the Sciences, New…
• Elected member of the National Academy of Sciences (2013)

Similar researchers at New York University

• David Hoggh-126
• Eero Simoncellih-113
• David Grierh-72