Last month, it was revealed that Google’s research in artificial neural networks (ANNs) was occasionally producing some truly weird images — and some users noticed right away what a striking resemblance these visuals bore to the ones people report after taking hallucinogenic drugs. It turns out the reason the ANNs “tripped out” is the same reason our brains do: with a reduced ability to judge the actual meaning of visuals as they’re processed (either because you’re high, or because you’re an experimental computer program), pattern recognition algorithms flail about.

The algorithms follow the simplest route from basic shapes to a guess at the objects those shapes represent, often getting off track without meaningful direction from the higher brain. Without some wisdom to go with the raw intellect of the computer, every roundish shape could, over many iterations, get categorized as an eye. And without subjective information about the context of a scene, otherwise totally functional algorithms can slowly turn clouds into creepy mutants on unicycles.

This example shows how the computational metaphor of an artificial neural network can grant basic insight into the workings of the brain — but scientific insight? ANNs are just now reaching the level of sophistication where they might be able to be used as a tool by scientists, allowing them to actually predict how the brain will react to changes in its structure. Now, an amazing new study from the Baylor College of Medicine claims to have done just that. When neuroscientists Ari Rosenberg and Jaclyn Sky Patterson simulated one theorized cause of autism in an artificial neural network, that simulation began exhibiting recognizably autism-like behavior.