Primates visually recognize and determine the category of an object at a brief glance, and to date, this behavior has been unmatched by artificial systems. A study published in PLOS Computational Biology has found that the latest artificial "deep neural network" performs as well as the primate brain at object recognition.

Charles Cadieu and colleagues from MIT measured the brain's object recognition ability by implanting arrays of electrodes in the inferior temporal cortex of macaques. This allowed the researchers to see the neural representation -- the population of neurons that respond -- for every object that the animals looked at.

When comparing these results with representations created by the deep neural networks, the accuracy of the model was determined by whether it grouped similar objects into similar clusters within the representation.

This improved understanding of how the primate brain works could lead to better artificial intelligence.

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