Neuroscientists in the McGovern Institute at MIT have been able to decipher a part of the code involved in recognizing visual objects.

In a fraction of a second, visual input about an object runs from the retina through increasingly higher levels of the visual stream, continuously reformatting the information until it reaches the highest purely visual level, the inferotemporal (IT) cortex. The IT cortex identifies and categorizes the object and sends that information to other brain regions.

To explore how the IT cortex formats that output, the researchers trained monkeys to recognize different objects grouped into categories, such as faces, toys and vehicles. The images appeared in different sizes and positions in the visual field. Recording the activity of hundreds of IT neurons produced a large database of IT neural patterns generated in response to each object under many different conditions.

Then the researchers used a computer algorithm, called a classifier, to decipher the code. The classifier was used to associate each object — say, a monkey’s face — with a particular pattern of neural signals, effectively decoding neural activity. Remarkably, the classifier found that just a split second’s worth of the neural signal contained specific enough information to identity and categorize the object, even at positions and sizes the classifier had not previously “seen.”

Practically speaking, computer algorithms used in artificial vision systems might benefit from mimicking these newly uncovered codes.

The study, a collaboration between James DiCarlo’s and Tomaso Poggio’s labs, appears in the Nov. 4 issue of Science.

Source: MIT news release