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Measuring the Alignment of ANNs and Primate V1 on Luminance and Contrast Response Characteristics
Stephanie Olaiya · Tiago Marques · James J DiCarlo

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in Shared Visual Representations in Human and Machine Intelligence (SVRHM) »
Event URL: https://openreview.net/forum?id=XTPfeOoZD8 »

Some artificial neural networks (ANNs) are the current state-of-the-art in modeling the primate ventral stream and object recognition behavior. However, how well they align with luminance and contrast processing in early visual areas is not known. Here, we compared luminance and contrast processing in ANN models of V1 and primate V1 at the level of single-neuron. Model neurons have luminance and contrast response characteristics that differ from those observed in macaque V1 neurons. In particular, model neurons have responses weakly modulated by changes in luminance and show non-saturating responses to high contrast stimuli. While no model perfectly matches macaque V1, there is great variability in their V1-alignment. Variability in luminance and contrast scores is not correlated suggesting that there are trade-offs in the model space of ANN V1 models.

Author Information

Stephanie Olaiya (Brown University)
Tiago Marques (MIT)
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.

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