Skip to Content
77 Mass Ave

Do-It-All Neurons

A key to cognitive flexibility.
August 21, 2013

Over the past few decades, neuroscientists have made great progress in mapping the brain by deciphering the functions of individual neurons that perform very specific tasks, such as recognizing the location or color of an object.

artist’s rendering of a neuron
Artist’s rendering of a neuron.

But many neurons, especially in brain regions that perform sophisticated functions such as thinking and planning, don’t fit into this pattern. Instead of responding exclusively to one stimulus or task, these neurons react in different ways to a wide variety of things.

MIT neuroscientist Earl Miller first noticed these unusual activity patterns about 20 years ago, while recording the electrical activity of neurons in animals trained to perform complex tasks. During one task, such neurons might distinguish between colors, but under different conditions, they might issue a motor command.

At the time, Miller and colleagues proposed that this type of neuronal flexibility is key to cognitive flexibility, which gives the brain its ability to learn so many new things on the fly. At first, that theory encountered resistance “because it runs against the traditional idea that you can figure out the clockwork of the brain by figuring out the one thing each neuron does,” Miller says.

In a recent paper published in Nature, Miller and colleagues at Columbia University described a computer model they developed to determine more precisely what role these flexible neurons play in cognition. They found that the cells are critical to the human brain’s ability to learn a large number of complex tasks.

Columbia professor Stefano Fusi created the model using experimental data gathered by Miller and his former grad student Melissa Warden, PhD ‘06. The data came from electrical recordings from brain cells of monkeys trained to look at a sequence of two pictures and remember the pictures and the order in which they appeared.

The computer model revealed that flexible neurons are critical to performing this kind of complex task, and they also greatly expand the capacity to learn many different things. In the computer model, neural networks without these flexible neurons could learn about 100 tasks before running out of capacity. That capacity expanded to tens of millions of tasks as flexible neurons were added to the model. When they reached about 30 percent of the total, the network’s capacity became “virtually unlimited,” Miller says—just like that of a human brain.

Keep Reading

Most Popular

This nanoparticle could be the key to a universal covid vaccine

Ending the covid pandemic might well require a vaccine that protects against any new strains. Researchers may have found a strategy that will work.

How do strong muscles keep your brain healthy?

There’s a robust molecular language being spoken between your muscles and your brain.

The 1,000 Chinese SpaceX engineers who never existed

LinkedIn users are being scammed of millions of dollars by fake connections posing as graduates of prestigious universities and employees at top tech companies.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.