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David J. Heeger

Researcher at New York University

Publications -  278
Citations -  41094

David J. Heeger is an academic researcher from New York University. The author has contributed to research in topics: Visual cortex & Visual system. The author has an hindex of 88, co-authored 268 publications receiving 38154 citations. Previous affiliations of David J. Heeger include Stanford University & Courant Institute of Mathematical Sciences.

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Journal ArticleDOI

Cortical Variability in the Sensory-Evoked Response in Autism

TL;DR: The robustness of the finding that individuals with autism spectrum disorder evince greater intra-individual variability in their sensory-evoked fMRI responses compared to typical control participants is explored, suggesting that greater cortical IIV may be a replicable characteristic of sensory systems in autism.
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A model of binocular rivalry and cross-orientation suppression

TL;DR: This work proposes a robust class of models that rely on ocular opponency neurons, previously proposed as a mechanism for efficient stereo coding, to yield rivalry only for dichoptic gratings, not for plaids.
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The effect of large veins on spatial localization with GE BOLD at 3 T: Displacement, not blurring.

TL;DR: Two different methods of region of interest (ROI) definition were used to investigate the spatial accuracy of functional magnetic resonance imaging (fMRI) at low and high spatial resolution, and the use of differential localizers did not necessarily result in a more accurate indication of the underlying neural activity.
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Orientation-selective adaptation to illusory contours in human visual cortex

TL;DR: Both early and higher-tier visual areas contain neurons selective for the orientation of this type of illusory contour, and orientation-selective adaptation to illusORY contours increased from early to higher- tier visual areas.
Proceedings ArticleDOI

Likelihood functions and confidence bounds for total-least-squares problems

TL;DR: The derivation of likelihood functions and confidence bounds for problems involving over-determined linear systems with noise in all measurements, often referred to as total-least-squares (TLS), are addressed.