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Related Experiment Video

Updated: Dec 19, 2025

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

A map of object space in primate inferotemporal cortex.

Pinglei Bao^1,2, Liang She³, Mason McGill⁴

¹Division of Biology and Biological Engineering, Tianqiao and Chrissy Chen Institute for Neuroscience, Caltech, Pasadena, CA, USA. pbao@caltech.edu.

|June 5, 2020

Summary

Researchers mapped the inferotemporal cortex (IT) using deep networks and brain recordings. They discovered a coarse map of object space, organizing visual object recognition across hierarchical stages.

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Last Updated: Dec 19, 2025

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

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Area of Science:

Neuroscience
Computational Neuroscience
Computer Vision

Background:

The inferotemporal (IT) cortex is crucial for object recognition.
The organizational principles governing IT cortex remain largely unknown, despite specialized areas for faces, bodies, and scenes.
Vast regions of IT cortex lack identified specialization, prompting investigation into general organizational principles.

Purpose of the Study:

To investigate the organizational principles of macaque IT cortex.
To understand how visual object representations are organized within the IT cortex.
To explore the relationship between deep neural networks and IT cortex organization.

Main Methods:

Functional magnetic resonance imaging (fMRI)
Microstimulation
Electrophysiology
Deep neural networks (DNNs) for object classification
Construction of a low-dimensional object space

Main Results:

IT cells were found to project objects onto specific axes within a DNN-derived object space.
Cells clustered into four networks based on preferred axes, forming a "map" of object space.
This map was consistently observed across three hierarchical stages with increasing view invariance.
These cell populations collectively demonstrated sufficient coding capacity for object reconstruction.

Conclusions:

Category-selective regions are integrated into a broader map of object space within the IT cortex.
The dimensions of this object space map can be derived from deep neural networks.
This provides a unified framework for understanding IT cortex organization in object recognition.