David Heeger

Last updated
David J. Heeger
Born1961 (age 62–63)
Berkeley, California, U.S.
Nationality American
Alma mater University of Pennsylvania
Awards David Marr Prize 1987, Alfred P. Sloan Research Fellowship 1994, Troland Research Award 2002, National Academy of Sciences 2013.
Scientific career
Fields Neuroscience (Visual Neuroscience, Computational Neuroscience, Systems Neuroscience, perceptual psychology, cognitive neuroscience, image processing, computer vision, computer graphics
Institutions New York University (professor)
Doctoral advisor Ruzena Bajcsy

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.

Contents

Research

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

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

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

Awards

Related Research Articles

<span class="mw-page-title-main">Visual cortex</span> Region of the brain that processes visual information

The visual cortex of the brain is the area of the cerebral cortex that processes visual information. It is located in the occipital lobe. Sensory input originating from the eyes travels through the lateral geniculate nucleus in the thalamus and then reaches the visual cortex. The area of the visual cortex that receives the sensory input from the lateral geniculate nucleus is the primary visual cortex, also known as visual area 1 (V1), Brodmann area 17, or the striate cortex. The extrastriate areas consist of visual areas 2, 3, 4, and 5.

Computational neuroscience is a branch of neuroscience which employs mathematics, computer science, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.

The consciousness and binding problem is the problem of how objects, background and abstract or emotional features are combined into a single experience.

<span class="mw-page-title-main">Barrel cortex</span> Region of the somatosensory cortex in some rodents and other species

The barrel cortex is a region of the somatosensory cortex that is identifiable in some species of rodents and species of at least two other orders and contains the barrel field. The 'barrels' of the barrel field are regions within cortical layer IV that are visibly darker when stained to reveal the presence of cytochrome c oxidase and are separated from each other by lighter areas called septa. These dark-staining regions are a major target for somatosensory inputs from the thalamus, and each barrel corresponds to a region of the body. Due to this distinctive cellular structure, organisation, and functional significance, the barrel cortex is a useful tool to understand cortical processing and has played an important role in neuroscience. The majority of what is known about corticothalamic processing comes from studying the barrel cortex, and researchers have intensively studied the barrel cortex as a model of neocortical column.

Multisensory integration, also known as multimodal integration, is the study of how information from the different sensory modalities may be integrated by the nervous system. A coherent representation of objects combining modalities enables animals to have meaningful perceptual experiences. Indeed, multisensory integration is central to adaptive behavior because it allows animals to perceive a world of coherent perceptual entities. Multisensory integration also deals with how different sensory modalities interact with one another and alter each other's processing.

A gamma wave or gamma rhythm is a pattern of neural oscillation in humans with a frequency between 25 and 140 Hz, the 40 Hz point being of particular interest. Gamma rhythms are correlated with large-scale brain network activity and cognitive phenomena such as working memory, attention, and perceptual grouping, and can be increased in amplitude via meditation or neurostimulation. Altered gamma activity has been observed in many mood and cognitive disorders such as Alzheimer's disease, epilepsy, and schizophrenia.

<span class="mw-page-title-main">Neural oscillation</span> Brainwaves, repetitive patterns of neural activity in the central nervous system

Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons. At the level of neural ensembles, synchronized activity of large numbers of neurons can give rise to macroscopic oscillations, which can be observed in an electroencephalogram. Oscillatory activity in groups of neurons generally arises from feedback connections between the neurons that result in the synchronization of their firing patterns. The interaction between neurons can give rise to oscillations at a different frequency than the firing frequency of individual neurons. A well-known example of macroscopic neural oscillations is alpha activity.

A neuronal ensemble is a population of nervous system cells involved in a particular neural computation.

Neuronal tuning refers to the hypothesized property of brain cells by which they selectively represent a particular type of sensory, association, motor, or cognitive information. Some neuronal responses have been hypothesized to be optimally tuned to specific patterns through experience. Neuronal tuning can be strong and sharp, as observed in primary visual cortex, or weak and broad, as observed in neural ensembles. Single neurons are hypothesized to be simultaneously tuned to several modalities, such as visual, auditory, and olfactory. Neurons hypothesized to be tuned to different signals are often hypothesized to integrate information from the different sources. In computational models called neural networks, such integration is the major principle of operation. The best examples of neuronal tuning can be seen in the visual, auditory, olfactory, somatosensory, and memory systems, although due to the small number of stimuli tested the generality of neuronal tuning claims is still an open question.

Matteo Carandini is a neuroscientist who studies the visual system. He is currently a professor at University College London, where he co-directs the Cortical Processing Laboratory with Kenneth D Harris.

The normalization model is an influential model of responses of neurons in primary visual cortex. David Heeger developed the model in the early 1990s, and later refined it together with Matteo Carandini and J. Anthony Movshon. The model involves a divisive stage. In the numerator is the output of the classical receptive field. In the denominator, a constant plus a measure of local stimulus contrast. Although the normalization model was initially developed to explain responses in the primary visual cortex, normalization is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions, including the representation of odors in the olfactory bulb, the modulatory effects of visual attention, the encoding of value, and the integration of multisensory information. It has also been observed at subthreshold potentials in the hippocampus. Its presence in such a diversity of neural systems in multiple species, from invertebrates to mammals, suggests that normalization serves as a canonical neural computation. Divisive normalization reduces the redundancy in natural stimulus statistics and is sometimes viewed as an implementation of the efficient coding principle. Formally, divisive normalization is an information-maximizing code for stimuli following a multivariate Pareto distribution.

<span class="mw-page-title-main">Supplementary motor area</span> Midline region in front of the motor cortex of the brain

The supplementary motor area (SMA) is a part of the motor cortex of primates that contributes to the control of movement. It is located on the midline surface of the hemisphere just in front of the primary motor cortex leg representation. In monkeys the SMA contains a rough map of the body. In humans the body map is not apparent. Neurons in the SMA project directly to the spinal cord and may play a role in the direct control of movement. Possible functions attributed to the SMA include the postural stabilization of the body, the coordination of both sides of the body such as during bimanual action, the control of movements that are internally generated rather than triggered by sensory events, and the control of sequences of movements. All of these proposed functions remain hypotheses. The precise role or roles of the SMA is not yet known.

<span class="mw-page-title-main">Filling-in</span>

In vision, filling-in phenomena are those responsible for the completion of missing information across the physiological blind spot, and across natural and artificial scotomata. There is also evidence for similar mechanisms of completion in normal visual analysis. Classical demonstrations of perceptual filling-in involve filling in at the blind spot in monocular vision, and images stabilized on the retina either by means of special lenses, or under certain conditions of steady fixation. For example, naturally in monocular vision at the physiological blind spot, the percept is not a hole in the visual field, but the content is “filled-in” based on information from the surrounding visual field. When a textured stimulus is presented centered on but extending beyond the region of the blind spot, a continuous texture is perceived. This partially inferred percept is paradoxically considered more reliable than a percept based on external input..

Flash suppression is a phenomenon of visual perception in which an image presented to one eye is suppressed by a flash of another image presented to the other eye.

<span class="mw-page-title-main">Connectome</span> Comprehensive map of neural connections in the brain

A connectome is a comprehensive map of neural connections in the brain, and may be thought of as its "wiring diagram". An organism's nervous system is made up of neurons which communicate through synapses. A connectome is constructed by tracing the neuron in a nervous system and mapping where neurons are connected through synapses.

<span class="mw-page-title-main">Neural correlates of consciousness</span> Neuronal events sufficient for a specific conscious percept

The neural correlates of consciousness (NCC) refer to the relationships between mental states and neural states and constitute the minimal set of neuronal events and mechanisms sufficient for a specific conscious percept. Neuroscientists use empirical approaches to discover neural correlates of subjective phenomena; that is, neural changes which necessarily and regularly correlate with a specific experience. The set should be minimal because, under the materialist assumption that the brain is sufficient to give rise to any given conscious experience, the question is which of its components are necessary to produce it.

The oddball paradigm is an experimental design used within psychology research. Presentations of sequences of repetitive stimuli are infrequently interrupted by a deviant stimulus. The reaction of the participant to this "oddball" stimulus is recorded.

Joseph Anthony Movshon is an American neuroscientist. He has made contributions to the understanding of the brain mechanisms that represent the form and motion of objects, and the way these mechanisms contribute to perceptual judgments and visually guided movement. He is a founding co-editor of the Annual Review of Vision Science.

The Karl Spencer Lashley Award is awarded by The American Philosophical Society as a recognition of research on the integrative neuroscience of behavior. The award was established in 1957 by a gift from Dr. Karl Spencer Lashley.

Andreas Karl Engel is a German neuroscientist. He is the director of the Department of Neurophysiology and Pathophysiology at the University Medical Center Hamburg-Eppendorf (UKE).

References

  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.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.