%0 Journal Article %J Proceedings of the National Academy of Sciences %D 2022 %T Face neurons encode nonsemantic features %A Bardon, Alexandra %A Will Xiao %A Carlos R Ponce %A Margaret S Livingstone %A Gabriel Kreiman %X

The primate inferior temporal cortex contains neurons that respond more strongly to faces than to other objects. Termed “face neurons,” these neurons are thought to be selective for faces as a semantic category. However, face neurons also partly respond to clocks, fruits, and single eyes, raising the question of whether face neurons are better described as selective for visual features related to faces but dissociable from them. We used a recently described algorithm, XDream, to evolve stimuli that strongly activated face neurons. XDream leverages a generative neural network that is not limited to realistic objects. Human participants assessed images evolved for face neurons and for nonface neurons and natural images depicting faces, cars, fruits, etc. Evolved images were consistently judged to be distinct from real faces. Images evolved for face neurons were rated as slightly more similar to faces than images evolved for nonface neurons. There was a correlation among natural images between face neuron activity and subjective “faceness” ratings, but this relationship did not hold for face neuron–evolved images, which triggered high activity but were rated low in faceness. Our results suggest that so-called face neurons are better described as tuned to visual features rather than semantic categories.

%B Proceedings of the National Academy of Sciences %V 119 %8 02/2022 %G eng %U https://pnas.org/doi/full/10.1073/pnas.2118705119 %N 16 %! Proc. Natl. Acad. Sci. U.S.A. %R 10.1073/pnas.2118705119 %0 Journal Article %J Cell %D 2019 %T Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences %A Carlos R Ponce %A Will Xiao %A Peter F Schade %A Till S. Hartmann %A Gabriel Kreiman %A Margaret S Livingstone %X

What specific features should visual neurons encode, given the infinity of real-world images and the limited number of neurons available to represent them? We investigated neuronal selectivity in monkey inferotemporal cortex via the vast hypothesis space of a generative deep neural network, avoiding assumptions about features or semantic categories. A genetic algorithm searched this space for stimuli that maximized neuronal firing. This led to the evolution of rich synthetic images of objects with complex combinations of shapes, colors, and textures, sometimes resembling animals or familiar people, other times revealing novel patterns that did not map to any clear semantic category. These results expand our conception of the dictionary of features encoded in the cortex, and the approach can potentially reveal the internal representations of any system whose input can be captured by a generative model.

%B Cell %V 177 %P 1009 %8 05/2019 %G eng %U https://www.cell.com/cell/fulltext/S0092-8674(19)30391-5 %& 999 %R 10.1016/j.cell.2019.04.005 %0 Journal Article %J Trends in Cognitive Sciences %D 2018 %T Cortex Is Cortex: Ubiquitous Principles Drive Face-Domain Development %A Margaret S Livingstone %A Michael J Arcaro %A Peter F Schade %K development %K face domains %K self-organizing systems %X

Powell, Kosakowski, and Saxe [1] argued in a recent review that two bottom-up models previously proposed to account for the development of face domains in inferotemporal cortex (IT) 2, 3 are insufficient to explain the existing data. They proposed instead that face domains are predisposed to process faces via selective connectivity to social information in medial prefrontal cortex. Here we explain why activity-dependent mechanisms acting on a retinotopic proto-architecture provide a sufficient explanation for the development of face, and other category, domains...

%B Trends in Cognitive Sciences %8 11/2018 %G eng %U https://linkinghub.elsevier.com/retrieve/pii/S1364661318302572 %! Trends in Cognitive Sciences %R 10.1016/j.tics.2018.10.009 %0 Journal Article %J Nature Neuroscience %D 2017 %T Seeing faces is necessary for face-domain formation %A Michael J Arcaro %A Peter F Schade %A Vincent, Justin L %A Carlos R Ponce %A Margaret S Livingstone %X

Here we report that monkeys raised without exposure to faces did not develop face domains, but did develop domains for other categories and did show normal retinotopic organization, indicating that early face deprivation leads to a highly selective cortical processing deficit. Therefore, experience must be necessary for the formation (or maintenance) of face domains. Gaze tracking revealed that control monkeys looked preferentially at faces, even at ages prior to the emergence of face domains, but face-deprived monkeys did not, indicating that face looking is not innate. A retinotopic organization is present throughout the visual system at birth, so selective early viewing behavior could bias category-specific visual responses toward particular retinotopic representations, thereby leading to domain formation in stereotyped locations in inferotemporal cortex, without requiring category-specific templates or biases. Thus, we propose that environmental importance influences viewing behavior, viewing behavior drives neuronal activity, and neuronal activity sculpts domain formation.

%B Nature Neuroscience %V 5631628 %8 09/2017 %G eng %U http://www.nature.com/doifinder/10.1038/nn.4635 %! Nat Neurosci %R 10.1038/nn.4635