Eero Simoncelli

About

Eero P Simoncelli is a Silver Professor and a Professor of Neural Science, Mathematics, Data Science, and Psychology at New York University. His research interests include computational neuroscience, visual and auditory perception, and statistical image and signal processing. He earned his Ph.D. in 1993 from the Massachusetts Institute of Technology. Dr. Simoncelli's work focuses on understanding the neural mechanisms underlying sensory perception and developing computational models that mimic these processes. His contributions have advanced the fields of neural science and signal processing, integrating interdisciplinary approaches to explore how the brain interprets complex sensory information.

Research topics

• Computer Science
• Artificial Intelligence
• Cognitive science
• Mathematics
• Biology
• Data science
• Psychology
• Computer vision
• Programming language

Selected publications

• Image Quality Assessment: Unifying Structure and Texture Similarity

  IEEE Transactions on Pattern Analysis and Machine Intelligence · 2020 · 852 citations

  Senior authorCorresponding

  Objective measures of image quality generally operate by comparing pixels of a "degraded" image to those of the original. Relative to human observers, these measures are overly sensitive to resampling of texture regions (e.g., replacing one patch of grass with another). Here, we develop the first full-reference image quality model with explicit tolerance to texture resampling. Using a convolutional neural network, we construct an injective and differentiable function that transforms images to mu…

  DOI

• Catalyzing next-generation Artificial Intelligence through NeuroAI

  Nature Communications · 2023 · 276 citations

  Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed…

  PublisherOA PDFDOI

• Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

  arXiv (Cornell University) · 2022 · 33 citations

  Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed…

  PublisherOA PDFDOI

• A unified framework for perceived magnitude and discriminability of sensory stimuli

  Proceedings of the National Academy of Sciences · 2024-06-10 · 17 citations

  articleOpen accessSenior authorCorresponding

  The perception of sensory attributes is often quantified through measurements of sensitivity (the ability to detect small stimulus changes), as well as through direct judgments of appearance or intensity. Despite their ubiquity, the relationship between these two measurements remains controversial and unresolved. Here, we propose a framework in which they arise from different aspects of a common representation. Specifically, we assume that judgments of stimulus intensity (e.g., as measured throu…

  PublisherOA PDFDOI

• Fixational eye movements enhance the precision of visual information transmitted by the primate retina

  Nature Communications · 2024-09-11 · 15 citations

  articleOpen access

  Fixational eye movements alter the number and timing of spikes transmitted from the retina to the brain, but whether these changes enhance or degrade the retinal signal is unclear. To quantify this, we developed a Bayesian method for reconstructing natural images from the recorded spikes of hundreds of retinal ganglion cells (RGCs) in the macaque retina (male), combining a likelihood model for RGC light responses with the natural image prior implicitly embedded in an artificial neural network op…

  PublisherOA PDFDOI

Recent grants

• Visual pattern representation in the extrastriate cortex

  NIH · $4.5M · 2013–2029

• CRCNS: Collaborative Research: Naturalistic computation and signaling by neural populations in the primate retina

  NSF · $100k · 2014–2018

Frequent coauthors

• J. Anthony Movshon

  New York University

  61 shared

• Timothy D. Oleskiw

  Flatiron Health (United States)

  50 shared

• Robbe L. T. Goris

  The University of Texas at Austin

  49 shared

• Johannes Ballé

  Google (United States)

  36 shared

• Alan A. Stocker

  University of Pennsylvania

  35 shared

• Corey M. Ziemba

  The University of Texas at Austin

  35 shared

• E. J. Chichilnisky

  Stanford University

  33 shared

• William F. Broderick

  Flatiron Institute

  33 shared

Education

• Ph.D., Electrical Engineering & Computer Science

  Massachusetts Institute of Technology

  1993

• M.S., Electrical Engineering & Computer Science

  Massachusetts Institute of Technology

  1988

• Certificate of Advanced Study, Mathematics

  University of Cambridge, King's College

  1986

• B.A., summa cum laude, Physics

  Harvard University

  1984

Similar researchers at New York University

• David Hoggh-126
• David Heegerh-95
• David Grierh-72