Neuro Topics - Machine learning
SEARCH OTHER RESEARCH AREAS
August 6, 2024
David Perez and colleagues (first authors Christiana Westlin and Andrew J Guthrie) used machine learning algorithms to compare the brains of patients with functional neurological disorder (FND) and healthy subjects or participants with psychiatric disorders that do not have FND, They found that structural brain MRI has the potential to be a clinically useful tool for diagnosing FND.
Original article in: Journal of Neurology, Neurosurgery and Psychiatry >
July 15, 2022
Harvard MCB Department news article on new research from the team of Mitsuko Watabe-Uchida and Nao Uchida, first author, Ryunosuke Amo, finding evidence that rodent brains exhibit a specific form of learning called temporal difference (TD) learning, widely used in both animal learning models and artificial intelligence.
Original article in: Nature Neuroscience >
June 30, 2022
Harvard Gazette article on soaring alcohol use during the pandemic. Marisa Silveri, Scott Hadland, Joji Suzuki, and Kevin Hill discussed alarming drinking trends, safe weekly limits, and approaches for cutting back or quitting.
Original article in: Nature Neuroscience >
January 3, 2022
How does the brain represent the visual world around us? In this study from the lab of Carlos Ponce, led by Olivia Rose and James Johnson, visual cortex neurons “team up” with machine learning models to generate synthetic images—revealing the density of information in such representations.
Original article in: Nature Communiccation >
December 9, 2021
Harvard Gazette article on the establishment of the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard, launched with a $500M gift from Priscilla Chan and Mark Zuckerberg. The Institute will be led by neurobiologist Bernardo Sabatini of Harvard Medical School and computer scientist Sham Kakade of the University of Washington, who will join the Harvard faculty in January 2022.
Original article in: Nature Communiccation >
July 29, 2021
Harvard Gazette article on research from the lab Dushan N. Wadduwage and colleagues about the development of a new process of computational imaging that uses complex algorithms and machine learning in order to get high-resolution images 100 to 1,000 times faster than other state-of-the-art technologies.
Original article in: Science Advances >
November 13, 2020
Tatsuya Tsukahara shares new research from the lab of Sandeep Robert Datta, on the use of their Motion Sequencing (MoSeq) behavioral analysis technique, built on 3D animal postures and unsupervised machine learning, to organize large and complex behavioral datasets from mice treated with neuroactive and psychoactive drugs.
Original article in: Nature Neuroscience >
November 2, 2020
Nature Technology Feature highlights the work of Jeff Lichtman, Aravi Samuel and other scientists across the world who are eager to apply advances in AI and microscopy to mapping the wiring diagrams of nervous systems.
Original article in: Nature Technology >
October 6, 2020
HMS News article on new research showing that a machine learning-based sleep test may help diagnose and predict dementia in older adults. From M. Brandon Westover, Alice Lam, and colleagues, first author Elissa Ye. Also featured in the Harvard Gazette.
Original article in: JAMA Network Open >
July 22, 2019
New research suggests that two simple, quantifiable measures — spontaneous fluctuations in pupil dilation or heart rate — could enable much earlier diagnosis of Rett syndrome and possibly other disorders with autism-like features.
Original article in: PNAS >
July 8, 2019
Harvard Gazette article on new research from Radcliffe Fellow Ani Patel suggesting that humans are not the only species who can dance to a beat.
Original article in: PNAS >
June 28, 2019
HMS News interview with Pascal Kaeser on the debut of a Wiki-like knowledge base for more than 1,000 synaptic genes.
Original article in: PNAS >
April 3, 2019
HMS News article on a new report from Isaac Kohane and colleagues at Google outlining the promises and pitfalls of machine learning in medicine.
Original article in: New England Journal of Medicine >