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David Heeger

Summary

David J. Heeger (born 1961) is an American neuroscientist, psychologist, computer scientist, data scientist, and entrepreneur. He is a professor at New York University, Chief Scientific Officer of Statespace Labs, and Chief Scientific Officer and co-founder of Epistemic AI.

David J. Heeger
Born1961 (age 62–63)
Berkeley, California, U.S.
NationalityAmerican
Alma materUniversity of Pennsylvania
AwardsDavid Marr Prize 1987, Alfred P. Sloan Research Fellowship 1994, Troland Research Award 2002, National Academy of Sciences 2013.
Scientific career
FieldsNeuroscience (Visual Neuroscience, Computational Neuroscience, Systems Neuroscience, perceptual psychology, cognitive neuroscience, image processing, computer vision, computer graphics
InstitutionsNew York University (professor)
Doctoral advisorRuzena Bajcsy

Research edit

Heeger's academic research spans a cross-section of engineering, psychology, and neuroscience. In the fields of perceptual psychology, systems neuroscience, cognitive neuroscience, and computational neuroscience, Heeger has developed computational theories of neuronal processing in the visual system, and he has performed psychophysics (perceptual psychology) and neuroimaging (functional magnetic resonance imaging, fMRI) experiments on human vision. His primary contribution to computational neuroscience is a theory of neural processing called the normalization model.[1][2] His experimental research has contributed to our understanding of the topographic organization of visual cortex (retinotopy),[3][4][5][6][7] visual awareness,[8][9][10] visual pattern detection/discrimination,[11][12] visual motion perception,[13][14][15] stereopsis (depth perception),[16] attention,[17][18][19][20] working memory, the control of eye and hand movements, neural processing of complex audio-visual and emotional experiences (movies, music, narrative),[21][22] abnormal visual processing in dyslexia,[23][24] and neurophysiological characteristics of autism.[25][26][27]

In the fields of image processing, computer vision, and computer graphics, Heeger has worked on motion estimation and image registration, wavelet image representations,[28] anisotropic diffusion (edge-preserving noise reduction),[29] image fidelity metrics (for evaluating image data compression algorithms), and texture analysis/synthesis.[30]

Heeger's current research focuses on developing and testing a unified theory of cortical circuit function. The field of neuroscience needs a general theory of brain function, like Maxwell's Equations for the brain. There is considerable evidence that the brain relies on a set of canonical neural circuits that perform a set of canonical neural computations, repeating them across brain regions and modalities to apply operations of the same form. But we lack a theoretical framework for how such canonical computations can support a wide variety of cognitive processes and brain functions. Heeger developed a class of circuit models, called Oscillatory Recurrent Gated Neural Integrator Circuits (ORGaNICs), that recapitulate many key neurophysiological and cognitive/perceptual phenomena including sensory processing and attention in visual cortex, working memory in prefrontal and parietal cortex, and premotor activity and motor control in motor cortex.[31][32][33] The theory offers a unified framework for the dynamics of neural activity, and it recapitulates many key neurophysiological and cognitive/perceptual phenomena (including normalization, oscillatory activity, sustained delay-period activity, sequential activity and traveling waves of activity), measured with a wide range of methodologies (including intracellular recordings of membrane potential fluctuations, firing rates of individual neurons, optogenetic manipulations, local field potentials, neuroimaging, and behavioral performance).

Education and career edit

Heeger holds a bachelor's degree in mathematics as well as a master's degree and doctorate in computer science—all from the University of Pennsylvania. He was a postdoctoral fellow at MIT, a research scientist at the NASA-Ames Research Center, and an associate professor at Stanford before joining NYU.

Personal life edit

His father, Alan J. Heeger, is an American physicist who was awarded the Nobel Prize in chemistry in 2000.

Awards edit

References edit

  1. ^ Carandini, M. and D.J. Heeger, Normalization as a canonical neural computation. Nat Rev Neurosci, 2012. 13(1): p. 51-62.
  2. ^ Heeger, D.J., Normalization of cell responses in cat striate cortex. Vis Neurosci, 1992. 9(2): p. 181-197.
  3. ^ Gardner, J.L., et al., Maps of visual space in human occipital cortex are retinotopic, not spatiotopic. J Neurosci, 2008. 28(15): p. 3988-99.
  4. ^ Larsson, J. and D.J. Heeger, Two retinotopic visual areas in human lateral occipital cortex. J Neurosci, 2006. 26(51): p. 13128-42.
  5. ^ Schluppeck, D., P. Glimcher, and D.J. Heeger, Topographic organization for delayed saccades in human posterior parietal cortex. J Neurophysiol, 2005. 94(2): p. 1372-84.
  6. ^ Silver, M.A., D. Ress, and D.J. Heeger, Topographic maps of visual spatial attention in human parietal cortex. J Neurophysiol, 2005. 94(2): p. 1358-71.
  7. ^ Huk, A.C., R.F. Dougherty, and D.J. Heeger, Retinotopy and functional subdivision of human areas MT and MST. J Neurosci, 2002. 22(16): p. 7195-7205.
  8. ^ Polonsky, A., et al., Neuronal activity in human primary visual cortex correlates with perception during binocular rivalry. Nat Neurosci, 2000. 3(11): p. 1153-9.
  9. ^ Lee, S.H., R. Blake, and D.J. Heeger, Traveling waves of activity in primary visual cortex during binocular rivalry. Nat Neurosci, 2005. 8(1): p. 22-3.
  10. ^ Lee, S.H., R. Blake, and D.J. Heeger, Hierarchy of cortical responses underlying binocular rivalry. Nat Neurosci, 2007. 10(8): p. 1048-54.
  11. ^ Ress, D. and D.J. Heeger, Neuronal correlates of perception in early visual cortex. Nat Neurosci, 2003. 10: p. 10.
  12. ^ Boynton, G.M., et al., Neuronal basis of contrast discrimination. Vision Res, 1999. 39(2): p. 257-69.
  13. ^ Huk, A.C., D. Ress, and D.J. Heeger, Neuronal basis of the motion aftereffect reconsidered. Neuron, 2001. 32(1): p. 161-72.
  14. ^ Huk, A.C. and D.J. Heeger, Pattern-motion responses in human visual cortex. Nat Neurosci, 2002. 5(1): p. 72-5.
  15. ^ Heeger, D.J., et al., Motion opponency in visual cortex. J Neurosci, 1999. 19(16): p. 7162-74.
  16. ^ Backus, B.T., et al., Human cortical activity correlates with stereoscopic depth perception. J Neurophysiol, 2001. 86(4): p. 2054-68.
  17. ^ Reynolds, J.H. and D.J. Heeger, The normalization model of attention. Neuron, 2009. 61(2): p. 168-85.
  18. ^ Herrmann, K., et al., When size matters: attention affects performance by contrast or response gain. Nat Neurosci, 2010. 13(12): p. 1554-9.
  19. ^ Ress, D., B.T. Backus, and D.J. Heeger, Activity in primary visual cortex predicts performance in a visual detection task. Nat Neurosci, 2000. 3(9): p. 940-945.
  20. ^ Gandhi, S.P., D.J. Heeger, and G.M. Boynton, Spatial attention affects brain activity in human primary visual cortex. Proc Natl Acad Sci U S A, 1999. 96(6): p. 3314-9.
  21. ^ Hasson, U., et al., A hierarchy of temporal receptive windows in human cortex. J Neurosci, 2008. 28(10): p. 2539-50.
  22. ^ Hasson, U., R. Malach, and D.J. Heeger, Reliability of cortical activity during natural stimulation. Trends Cogn Sci, 2010. 14(1): p. 40-8.
  23. ^ Demb, J.B., G.M. Boynton, and D.J. Heeger, Brain activity in visual cortex predicts individual differences in reading performance. Proc Natl Acad Sci U S A, 1997. 94(24): p. 13363-6.
  24. ^ Demb, J.B., G.M. Boynton, and D.J. Heeger, Functional magnetic resonance imaging of early visual pathways in dyslexia. J Neurosci, 1998. 18(17): p. 6939-51.
  25. ^ Dinstein, I., et al., A mirror up to nature. Curr Biol, 2008. 18(1): p. R13-8.
  26. ^ Dinstein, I., et al., Normal movement selectivity in autism. Neuron, 2010. 66(3): p. 461-9.
  27. ^ Dinstein, I., et al., Unreliable evoked responses in autism. Neuron, 2012. 75(6): p. 981-91.
  28. ^ Simoncelli, E.P., et al., Shiftable multi-scale transforms. IEEE Transactions on Information Theory, Special Issue on Wavelets, 1992. 38: p. 587-607.
  29. ^ Black, M., et al., Robust anisotropic diffusion. IEEE Transactions on Image Processing, 1998. 7: p. 421-432.
  30. ^ Heeger, D.J. and J.R. Bergen. Pyramid-Based Texture Analysis/Synthesis. in Computer Graphics, SIGGRAPH Proceedings. 1995.
  31. ^ Heeger, David J. (2017-02-21). "Theory of cortical function". Proceedings of the National Academy of Sciences of the United States of America. 114 (8): 1773–1782. Bibcode:2017PNAS..114.1773H. doi:10.1073/pnas.1619788114. ISSN 1091-6490. PMC 5338385. PMID 28167793.
  32. ^ Heeger, David J.; Mackey, Wayne E. (2019-11-05). "Oscillatory recurrent gated neural integrator circuits (ORGaNICs), a unifying theoretical framework for neural dynamics". Proceedings of the National Academy of Sciences of the United States of America. 116 (45): 22783–22794. Bibcode:2019PNAS..11622783H. doi:10.1073/pnas.1911633116. ISSN 1091-6490. PMC 6842604. PMID 31636212.
  33. ^ Heeger, David J.; Zemlianova, Klavdia O. (2020-09-08). "A recurrent circuit implements normalization, simulating the dynamics of V1 activity". Proceedings of the National Academy of Sciences of the United States of America. 117 (36): 22494–22505. Bibcode:2020PNAS..11722494H. doi:10.1073/pnas.2005417117. ISSN 1091-6490. PMC 7486719. PMID 32843341.

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