"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."
"Nearly a decade later, Miller - alongside researchers from Dartmouth, including Dr. Anand Pathak and Prof. Richard Granger - gave the same task to a very different subject. It wasn't a primate at all, but a computational model that the team wired to work like the real brain circuits that control learning and decision-making. Dr. Miller and his colleagues hoped it would produce patterns of neural activity similar to what they observed in the macaques."
Macaque monkeys performed a standardized visual categorization task while researchers recorded neural activity across learning. Years later, a computational model wired to emulate brain circuits for learning and decision-making performed the same task and produced realistic neural activity without neural training data. The model revealed a group of neurons that strengthened while predicting incorrect choices; the same pattern appeared in the original macaque recordings. Those signals were labeled incongruent neurons (ICNs), suggesting brains preserve alternate, incorrect options during learning. The model's brain-like behavior supports its use for probing learning dynamics and testing neurological interventions.
Read at Big Think
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