Proceedings of the National Academy of Sciences of the United States of America

About the PNAS Member Editor
Name Heeger, David J.
Location New York University
Primary Field Psychological and Cognitive Sciences
Secondary Field Systems Neuroscience
 Election Citation
Heeger has made fundamental, interrelated contributions in three areas of vision science: image processing, visual perception, and visual neuroscience. He uses advances in understanding human vision to develop new approaches to image processing and he uses the applied mathematics of image processing to develop and test theories of human vision.
 Research Interests
Heeger's research spans an interdisciplinary cross-section of engineering, psychology, and neuroscience. He has studied visual perception and visual neuroscience, cognitive neuroscience, computational neuroscience, computer vision, image processing, computer graphics, AI, artificial neural networks, and data science. His current research is focused on understanding the computations performed by neural circuits in the brain. There is considerable evidence that the brain relies on 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, brain functions, and neural systems. The field of neuroscience needs a general theory of brain function, like Maxwell?s Equations for the brain. Heeger is developing such a theoretical framework. The theory offers a unified framework for the dynamics of neural activity, and it recapitulates many key neurophysiological and cognitive/perceptual phenomena (including sensory processing and attention in visual cortex, and working memory in prefrontal cortex), 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).

 
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