2026-04-22 カリフォルニア工科大学（Caltech）
The top illustration shows how Yuelin Shi (PhD ’26) and a team of researchers saw populations of face cells transition from detection to discrimination by switching from an object-general code to a face-specific one in a very short period of time. On the bottom, the schematic shows the predictions of domain-general (left) and domain-specific (right) models of face processing. If cells use a domain-general mechanism (left), the face and object axes should align. If cells use a domain-specific mechanism (right), the face and object axes should differ.Credit: Adapted from Shi et al. Nature (2026), CC BY 4.0.
＜関連情報＞
- https://www.caltech.edu/about/news/fixed-or-flexible-study-shows-vision-related-neurons-can-rapidly-switch-codes
- https://www.nature.com/articles/s41586-026-10267-3
- https://www.nature.com/articles/s41586-020-2350-5
- https://www.cell.com/cell/fulltext/S0092-8674(17)30538-X
下側頭皮質における神経コードの迅速かつ協調的な切り替え Rapid concerted switching of the neural code in the inferotemporal cortex
Yuelin Shi,Dasheng Bi,Janis K. Hesse,Frank F. Lanfranchi,Shi Chen & Doris Y. Tsao
Nature Published:25 March 2026
DOI:https://doi.org/10.1038/s41586-026-10267-3
Abstract
A fundamental paradigm in neuroscience is that neurons represent the world through fixed tuning functions, with stable mappings from stimulus features to firing rates1. Here, we report that tuning can instead shift rapidly and coherently across a neural population, enabling a dynamic transition from detecting a broad category to discriminating individual exemplars. We set out to address a longstanding debate in visual neuroscience about whether the inferotemporal cortex uses a specialized code for specific object categories or a general-purpose code that applies to all objects. We found that face-selective cells in macaque inferotemporal cortex initially adopted a general code optimized for face detection. However, after a rapid concerted population event lasting less than 20 ms, the neural code transformed into a face-specific one, with two striking features: response gradients to principal detection-related dimensions reversed direction, and new tuning emerged for multiple higher-dimensional features that support fine face discrimination. These dynamics in face patches were specific to face stimuli and did not occur in response to non-face objects. Thus, for faces, face cells transition from detection to discrimination by switching from an object-general code to a face-specific one. More broadly, our findings indicate that there is a previously unknown mechanism for neural representation: concerted stimulus-dependent switching of the neural code used by a cortical area.
霊長類の側頭葉下部皮質における物体空間の地図 A map of object space in primate inferotemporal cortex
Pinglei Bao,Liang She,Mason McGill & Doris Y. Tsao
Nature Published:03 June 2020
DOI:https://doi.org/10.1038/s41586-020-2350-5
Abstract
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 found1,2,3,4,5, 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.
霊長類の脳における顔認識のコード The Code for Facial Identity in the Primate Brain
Le Chang ∙ Doris Y. Tsao
Cell Published:Published: June 1, 2017
DOI:https://doi.org/10.1016/j.cell.2017.05.011
Highlights
- Facial images can be linearly reconstructed using responses of ∼200 face cells
- Face cells display flat tuning along dimensions orthogonal to the axis being coded
- The axis model is more efficient, robust, and flexible than the exemplar model
- Face patches ML/MF and AM carry complementary information about faces
Summary
Primates recognize complex objects such as faces with remarkable speed and reliability. Here, we reveal the brain’s code for facial identity. Experiments in macaques demonstrate an extraordinarily simple transformation between faces and responses of cells in face patches. By formatting faces as points in a high-dimensional linear space, we discovered that each face cell’s firing rate is proportional to the projection of an incoming face stimulus onto a single axis in this space, allowing a face cell ensemble to encode the location of any face in the space. Using this code, we could precisely decode faces from neural population responses and predict neural firing rates to faces. Furthermore, this code disavows the long-standing assumption that face cells encode specific facial identities, confirmed by engineering faces with drastically different appearance that elicited identical responses in single face cells. Our work suggests that other objects could be encoded by analogous metric coordinate systems.
