To find out which sights specific neurons in monkeys “like” best, researchers designed an algorithm, called XDREAM, that generated images that made neurons fire more than any natural images the researchers tested. As the images evolved, they started to look like distorted versions of real-world stimuli.
Researchers have known that neurons in the visual cortex of primate brains respond to complex images, like faces, and that most neurons are quite selective in their image preference. Earlier studies on neuronal preference used many natural images to see which images caused neurons to fire most. However, this approach is limited by the fact that one cannot present all possible images to understand what exactly will best stimulate the cell.
The XDREAM algorithm uses the firing rate of a neuron to guide the evolution of a novel, synthetic image. It goes through a series of images over the course of minutes, mutates them, combines them, and then shows a new series of images. At first, the images looked like noise, but gradually they changed into shapes that resembled faces or something recognizable in the animal’s environment, like the food hopper in the animals’ room or familiar people wearing surgical scrubs. The algorithm was developed by Will Xiao in the laboratory of Gabriel Kreiman at Children’s Hospital and tested on real neurons at Harvard Medical School.
From this study, the researchers believe they are seeing that the brain learns to abstract statistically relevant features of its world. “We are seeing that the brain is analyzing the visual scene, and driven by experience, extracting information that is important to the individual over time,” says Ponce. “The brain is adapting to its environment and encoding ecologically significant information in unpredictable ways.”
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