Amy Adams, Director of Interdisciplinary Life Sciences Communications writes, "A computer model of brain function helps explain a 20-year-old finding
that the way a single noisy neuron fires in the brain can predict an
animal's decisions. It turns out neurons without noise can't learn. The
type of learning the group modeled reflects the way we learn to
categorize food, music or favorite cafes."
|Stanford research associate Tatiana Engel, PhD, is first author on a study showing how "noisy neurons" affect decision-making processes. Photo: Stanford Report|
Almost 20 years ago, Professor William Newsome, director of the Stanford Neurosciences Institute, stumbled on a surprising finding: Neurons in the brain have fluctuating, "noisy" signals, sometimes firing one way when faced with a certain stimuli and sometimes firing another. What's more, how that single, somewhat variable neuron fires is then reflected in an animal's decisions.
Now Stanford scientists have employed computer models to reveal the reason behind these "noisy neurons": neurons that are entirely consistent can't learn.
Postdoctoral scholar Tatiana Engel was first author on this work, which was published in last week's Nature Communications with colleagues from Yale, New York University and the University of Chicago.
Engel said that Newsome's original discovery was a surprise in part because the area where these noisy neurons reside – roughly above and behind the ear – is not a part of the brain normally associated with thoughtful decision-making.
"These cells are in the sensory system, so it's not in the cortex where we would like to think the decisions are made," said Engel, who did the work in the lab of Xiao-Jing Wang at Yale University. "This was exciting to me to realize that we are used to thinking about ourselves as agents who are in charge of our decisions and in charge of our thoughts, but the brain might be playing tricks with us."
Source: Stanford Report