“We saw some peculiar brain activity in the model,” Miller says. “There was a group of neurons that predicted the wrong answer, yet they kept getting stronger as the model learned. So we went back to the original macaque data, and the same signal was there, hiding in plain sight. It wasn’t a quirk of the model — the monkeys’ brains were doing it too. Even as their performance improved, both the real and simulated brains maintained a reserve of neurons that continued to predict the incorrect answer.”
The new work, published in Nature Communications, puts a name to these overlooked signals: incongruent neurons, or ICNs, and explores theories as to why a primate brain might want to keep alternate options in mind, even if they’re not the right ones at the moment.
Beyond identifying a previously unrecognized class of neurons involved in learning, the study shows that the model behaves like a brain and generates realistic brain activity, even without being trained on neural data. The findings could have major implications for testing potential neurological drugs and for using computational models to investigate how cognition emerges and functions.
