To figure this out, scientists first showed monkeys a set of pictures and recorded which face cells -- neurons that specifically respond to faces -- fired and which didn't. What they found was that single cells weren't responding to single faces. Instead, each cell was encoding a vector, or one direction in facial space, which means that a single neuron may respond only to a certain distance between a person's eyes or a dimple on the left side of the mouth.

So, while there are tens of thousands of neurons tuned to facial features, you only need around 200 to uniquely encode any given face. The signals from just that small number of cells can encode enough information to be able to differentiate a new face from all of the others the brain has encountered.

Once they knew what facial characteristic each cell was responding to, the researchers were then able to show a monkey a new face, record the cellular activity in response to it and reconstruct the face based on what the neurons were doing. And they were able to do those reconstructions with remarkable accuracy.

They were also able to create faces that looked completely different from each other but as long as those faces shared one specific feature, the researchers could get the same neuron to fire in response to all of them. So, if entirely different faces had the same crooked hairline, for example, the same neuron would fire in response to every face.

While these findings still need to be replicated, this work could help inform facial recognition technologies and AI. Le Chang, one of the researchers on the project said in a press release, "One can also imagine applications in forensics where one could reconstruct the face of a criminal by analyzing a witness's brain activity."

Check out this video, to hear how a single neuron responds to a set of images.