If you’ve ever forgotten something mere seconds after it was at the forefront of your mind—the name of a dish you were about to order at a restaurant, for instance—then you know how important working memory is. This type of short-term recall is how people retain information for a matter of seconds or minutes to solve a problem or carry out a task, like the next step in a series of instructions. But, although it’s critical in our day-to-day lives, exactly how the brain manages working memory has been a mystery.
Now, Salk scientists have developed a new computational model showing how the brain maintains information short-term using specific types of neurons. Their findings, published in Nature Neuroscience on December 7, 2020, could help shed light on why working memory is impaired in a broad range of neuropsychiatric disorders, including schizophrenia, as well as in normal aging.
“Most research on working memory focuses on the excitatory neurons in the cortex, which are numerous and broadly connected, rather than the inhibitory neurons, which are locally connected and more diverse,” says Terrence Sejnowski, head of Salk’s Computational Neurobiology Laboratory and senior author of the new work. “However, a recurrent neural network model that we taught to perform a working memory task surprised us by using inhibitory neurons to make correct decisions after a delay.”
In the new paper, Sejnowski and Robert Kim, a Salk and UC San Diego MD/Ph.D. student, developed a computer model of the prefrontal cortex, an area of the brain known to manage working memory. The researchers used learning algorithms to teach their model to carry out a test typically used to gauge working memory in primates—the animals must determine whether a pattern of colored squares on a screen matches one that was seen