Ward Pettibone, a neuroscientist and PhD candidate at UCLA, has studied gist extraction and rule extrapolation after sleep, two ways the brain reworks a memory overnight.
Both topics belong to research on memory consolidation, the process through which sleep helps new memories last. Gist extraction is the brain’s ability to pull a general rule out of several specific experiences.
Rule extrapolation is the next step: applying that rule to a situation the learner has not met before.
His undergraduate research at New York University examined episodic-like memory, and at Mount Sinai he studied motor gist learning and sleep.
Slow waves and spindles organize non-REM sleep
Slow-wave sleep is the deepest stage of non-REM sleep. During it, large groups of cortical neurons alternate between firing together and falling silent together.
That alternation produces the slow oscillation, a tall, unhurried wave in the brain’s electrical signal. Sleep spindles are short bursts of faster activity that appear during non-REM sleep.
A loop of activity between the thalamus and the cortex generates spindles. Studies of memory consolidation during sleep have linked spindle activity to how well people retain material learned before sleep.
One widely studied model centers on timing. Spindles nest inside slow waves, and brief hippocampal events called sharp-wave ripples nest inside the spindles.
Under that model, the hippocampus replays recent experience while the cortex is primed to receive it. The hippocampus holds new memories first, and the cortex stores longer-lasting knowledge.
REM sleep follows a different pattern. Its electrical signal is faster and lower in amplitude, closer to the signal of a waking brain.
Four brain regions are scored separately
Sleep states unfold over minutes to hours in a set pattern. Pettibone’s current UCLA project tracks that pattern in four regions at once: the prefrontal cortex, motor cortex, hippocampus and striatum.
He designed and built custom local field potential electrodes to capture the four simultaneously. The signal from each one is scored on its own in 10-second epochs.
Every epoch is assigned one of four states. The options are wake, slow-wave sleep, REM and a transition-to-REM stage that bridges the two sleep states.
When at least one region carries a different label from the others, that epoch is classed as local sleep. Researchers have observed local sleep in humans and in rodents.
It differs from the unihemispheric sleep of birds and dolphins. In unihemispheric sleep, one half of the brain sleeps while the other half stays awake.
A spatial task ties learning to the night that follows
Little is known about the purpose of local sleep. Sleep in general, by contrast, has a large body of evidence linking it to learning and memory.
The project tests whether local sleep affects the ability to learn. It also tests the reverse, whether learning changes the local sleep that comes after it.
Subjects learn the Hidden Goal Task, in which staying inside an unmarked location earns a reward. The task runs in a fully automated apparatus Pettibone built and programmed to his own design.
After each session, the four regions are monitored together while subjects have a chance to sleep. Monitoring continues for about 23 hours a day, so sleep in both the light and dark phases is captured.
The rewarded location moves every day
The protocol repeats for seven days. Each day opens with a retest on the location learned the day before, and then the rewarded location changes.
That sequence pairs every sleep period with a place memory that is first retrieved and then revised. Pettibone’s current work covers place recall and the flexible updating of place memories.
The project has produced more than 10,000 hours of sleep recordings, and analysis is ongoing. Electrophysiology and behavioral assays generate the data, and machine learning is part of his analytic toolkit.
Six publications and honors since 2015
Pettibone is an author on six research publications. Among his honors is the Frontiers in Neuroscience Editor’s Pick Collection (2021).
In 2024, he won the Poster Award at the UCLA BRI Future of Neuroscience conference. He also received the UCLA BRI/Semel Institute Graduate Student Travel Award that year.
He received the UCLA BRI Flexible Travel Award in 2023 and the Science Communication Award, UCLA, in 2021. His 2020 invited talk at The Museum of Science in Boston was titled Multiverse: Your Brain on Sleep.
His earliest listed honors date to New York University. He held a Dean’s Undergraduate Research Fund Grant there in 2015-16.
In 2016, he received the Sigma Xi Undergraduate Division Award (The Scientific Research Honor Society).
Pettibone’s research on sleep and memory consolidation runs from motor gist learning at Mount Sinai to local sleep at UCLA. At Mount Sinai, his work also covered tau tangles.
Place memory is the setting for his current questions about how sleep supports memory consolidation. The Hidden Goal Task asks subjects to recall one location and then learn a new one.
About Ward Pettibone
Ward Pettibone is a neuroscientist and PhD candidate at UCLA in Los Angeles, California, who studies sleep, learning, and memory. He holds a bachelor’s degree in neuroscience from New York University and previously worked as an associate researcher at Mount Sinai. His research and publications are summarized on Pettibone’s official website.
