Primates visually recognise and determine the category of an object even at a brief glance, and to date, this behaviour has been unmatched by artificial systems. A study publishing this week 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 and provide insight into understanding primate visual processing.
"The fact that the models predict the neural responses and the distances of objects in neural population space shows that these models encapsulate our current best understanding as to what is going on in this previously mysterious portion of the brain," say the authors.
Source: PLOS