Humans recognize visually-presented objects rapidly and accurately. To understand this ability, we seek to construct models of the ventral stream, the series of cortical areas thought to subserve object recognition. One tool to assess the quality of a model of the ventral stream is the Representation Dissimilarity Matrix (RDM), which uses a set of visual stimuli and measures the distances produced in either the brain (i.e. fMRI voxel responses, neural firing rates) or in models (features). Previous work has shown that all known models of the ventral stream fail to capture the RDM pattern observed in either IT cortex, the highest ventral area, or in the human ventral stream. In this work, we construct models of the ventral stream using a novel optimization procedure for category-level object recognition problems, and produce RDMs resembling both macaque IT and human ventral stream. The model, while novel in the optimization procedure, further develops a long-standing functional hypothesis that the ventral visual stream is a hierarchically arranged series of processing stages optimized for visual object recognition.
Daniel L Yamins (Massachusetts Institute of Technology)
Ha Hong (Massachusetts Institute of Technology)
Charles Cadieu (Massachusetts Institute of Technology)
James J DiCarlo (Massachusetts Institute of Technology)
Prof. DiCarlo received his Ph.D. in biomedical engineering and his M.D. from Johns Hopkins in 1998, and did his postdoctoral training in primate visual neurophysiology at Baylor College of Medicine. He joined the MIT faculty in 2002. He is a Sloan Fellow, a Pew Scholar, and a McKnight Scholar. His lab’s research goal is a computational understanding of the brain mechanisms that underlie object recognition. They use large-scale neurophysiology, brain imaging, optogenetic methods, and high-throughput computational simulations to understand how the primate ventral visual stream is able to untangle object identity from other latent image variables such as object position, scale, and pose. They have shown that populations of neurons at the highest cortical visual processing stage (IT) rapidly convey explicit representations of object identity, and that this ability is reshaped by natural visual experience. They have also shown how visual recognition tests can be used to discover new, high-performing bio-inspired algorithms. This understanding may inspire new machine vision systems, new neural prosthetics, and a foundation for understanding how high-level visual representation is altered in conditions such as agnosia, autism and dyslexia.
More from the Same Authors
2019 Poster: Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs »
Jonas Kubilius · Martin Schrimpf · Kohitij Kar · Rishi Rajalingham · Ha Hong · Najib Majaj · Elias Issa · Pouya Bashivan · Jonathan Prescott-Roy · Kailyn Schmidt · Aran Nayebi · Daniel Bear · Daniel Yamins · James J DiCarlo
2019 Oral: Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs »
Jonas Kubilius · Martin Schrimpf · Ha Hong · Najib Majaj · Rishi Rajalingham · Elias Issa · Kohitij Kar · Pouya Bashivan · Jonathan Prescott-Roy · Kailyn Schmidt · Aran Nayebi · Daniel Bear · Daniel Yamins · James J DiCarlo
2018 Poster: Task-Driven Convolutional Recurrent Models of the Visual System »
Aran Nayebi · Daniel Bear · Jonas Kubilius · Kohitij Kar · Surya Ganguli · David Sussillo · James J DiCarlo · Daniel Yamins
2013 Tutorial: Mechanisms Underlying Visual Object Recognition: Humans vs. Neurons vs. Machines »
James J DiCarlo