A map of object space in primate inferotemporal cortex

By Pinglei Bao, Liang She, Mason McGill, Doris Y. Tsao. Nature, 2020

368 citations recorded by the source corpus.

The inferotemporal (IT) cortex is responsible for object recognition, but it is unclear how the representation of visual objects is organized in this part of the brain. Areas that are selective for categories such as faces, bodies, and scenes have been found1–5, but large parts of IT cortex lack any known specialization, raising the question of what general principle governs IT organization. Here we used functional MRI, microstimulation, electrophysiology, and deep networks to investigate the organization of macaque IT cortex. We built a low-dimensional object space to describe general objects using a feedforward deep neural network trained on object classification6. Responses of IT cells to a large set of objects revealed that single IT cells project incoming objects onto specific axes of this space. Anatomically, cells were clustered into four networks according to the first two components of their preferred axes, forming a map of object space. This map was repeated across three hierarchical stages of increasing view invariance, and cells that comprised these maps collectively harboured sufficient coding capacity to approximately reconstruct objects. These results provide a unified picture of IT organization in which category-selective regions are part of a coarse map of object space whose dimensions can be extracted from a deep network. Primate inferotemporal cortex contains a coarse map of object space consisting of four networks, identified using functional imaging, electrophysiology and deep networks.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/c10055af62dec8c01a24536fdc3d8bc4c10a54c3

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Citations in the corpus

The neural architecture of language: Integrative modeling converges on predictive processing (2020, background): “…approach to computational modeling in systems neuroscience has transformed our algorithmic understanding of the primate ventral visual stream (Bao et al., 2020; Cadena et al., 2019; Cichy et al., 2016; Kietzmann et al., 2019; Kubilius et al., 2019; Schrimpf et al., 2018, 2020; Yamins et…”
Individual differences among deep neural network models (2020, methodology): “To expand our experiments to network architectures commonly used to predict brain data 3,4,13 – 15 , we trained and tested 10 network instances of a recent version of AlexNet 16 on a large-scale object classi ﬁ cation data set ILSVRC 2012 17 .”