When we have a new experience, the memory of that event is stored in a neural circuit that connects several parts of the hippocampus and other brain structures. Previous research has shown that encoding these memories involves cells in a part of the hippocampus called CA1, which then relays information to another brain structure called the entorhinal cortex. In one group of mice, the MIT team inhibited neurons of the subiculum as the mice underwent fear conditioning, which had no effect on their ability to later recall the experience. However, in another group, they inhibited subiculum neurons after fear conditioning had occurred, when the mice were placed back in the original chamber.
Feng Zhang, a member of the McGovern Institute for Brain Research and an associate professor in the Departments of Brain and Cognitive Sciences and of Biological Engineering, has been named a winner of the 2017 Albany Medical Center Prize in Medicine and Biomedical Research. Zhang, who is the Poitras Professor in Neuroscience at MIT and a core member of the Broad Institute, is recognized for his contributions to the development of CRISPR-Cas9 as a gene editing technology, which in the words of the prize announcement "has revolutionized biomedical research and provided new hope for the treatment of genetic diseases and more." The $500,000 prize has been given annually since 2001 to those who have altered the course of medical research, and is one of the largest prizes in medicine and science in the United States. In announcing the award, the Dean of Albany Medical College, Vincent Verdile, said: "Rarely has such a recent discovery transformed an entire field of research, as CRISPR has in biological research.
Along those lines, Alizadeh and his team at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed "Pensieve," an artificial intelligence (AI) system that uses machine learning to pick different algorithms depending on network conditions. "Our system is flexible for whatever you want to optimize it for," says PhD student Hongzi Mao, who was lead author on a related paper with Alizadeh and PhD student Ravi Netravali. Researchers have also tried to combine the two methods: A system out of Carnegie Mellon University outperforms both schemes using "model predictive control" (MPC), an approach that aims to optimize decisions by predicting how conditions will evolve over time. Content providers like YouTube could customize Pensieve's reward system based on which metrics they want to prioritize for users.
More than 50 million Americans suffer from sleep disorders, and diseases including Parkinson's and Alzheimer's can also disrupt sleep. To make it easier to diagnose and study sleep problems, researchers at MIT and Massachusetts General Hospital have devised a new way to monitor sleep stages without sensors attached to the body. Their device uses an advanced artificial intelligence algorithm to analyze the radio signals around the person and translate those measurements into sleep stages: light, deep, or rapid eye movement (REM). Recent advances in artificial intelligence have made it possible to train computer algorithms known as deep neural networks to extract and analyze information from complex datasets, such as the radio signals obtained from the researchers' sensor.
The Darwin Project, an alliance between oceanographers and microbiologists in the MIT Department of Earth, Atmospheric and Planetary Sciences (EAPS) and the Parsons Lab in the MIT Department of Civil and Environmental Engineering, was conceived as an initiative to "advance the development and application of novel models of marine microbes and microbial communities, identifying the relationships of individuals and communities to their environment, connecting cellular-scale processes to global microbial community structure" with the goal of coupling "state of the art physical models of global ocean circulation with biogeochemistry and genome-informed models of microbial processes." The boost in computational infrastructure the award provides for will advance several linked areas of research, including the capacity to model marine microbial systems in more detail, enhanced fidelity of the modeled fluid dynamical environment, support for state of the art data analytics including machine learning techniques, and accelerating and extending genomic data processing capabilities. As an initiative to advance our understanding of the biology, ecology, and biogeochemistry of microbial processes that dominate Earth's largest biome -- the global ocean -- SCOPE seeks to measure, model, and conduct experiments at a model ecosystem site located 100 km north of the Hawaiian island of Oahu that is representative of a large portion of the North Pacific Ocean. Steady growth in available large-scale metagenomic and single-cell genomic data resulting from genetics data activities in the Chisholm Lab are also driving additional computational processing resource needs.
It uses gasoline to generate the power that drives the lift motors, keeps backup batteries charged, and powers onboard electronics including computing, sensors, and communications equipment. Over the past several years, Top Flight has continued to develop major innovations for the microscale hybrid engine concept, called a "digital gearbox." Gasoline runs to a small generator, creating electric power, which the digital gearbox controls and sends in pulses to the electric motors and electronics. Immediate applications for Top Flight's drone capabilities may include inspecting infrastructure in remote areas.
Such precise control of printed objects' microstructure gives designers commensurate control of the objects' physical properties -- such as their density or strength, or the way they deform when subjected to stresses. The system thus takes advantage of physical measurements at the microscopic scale, while enabling computationally efficient evaluation of macroscopic designs. The building blocks from which Zhu and his colleagues assemble larger printable objects are clusters of voxels. For a given set of printable materials, they randomly generate clusters that combine those materials in different ways: a square of material A at the cluster's center, a border of vacant voxels around that square, material B at the corners, or the like.
A three-day hackathon on campus brought together students and researchers from MIT and around Boston who developed functional fabric concepts to solve major issues facing soldiers in combat or training, first responders, victims and workers in refugee camps, and many others. Remote Triage, formed by MIT students, designed an automated triage system for field medics, consisting of sensor-laden clothing that detects potential injury and a web platform that prioritizes care. The other team, Security Blanket, designed a double-sided, multipurpose blanket for people displaced from their homes, based on an idea from a Drexel University student. On Friday night, hackathon participants listened to talks from various experts -- including military officers, first responders, and government representatives -- who described major challenges they face in their fields.
In tests involving a new Google algorithm for producing high-dynamic-range images, which capture subtleties of color lost in standard digital images, the new system produced results that were visually indistinguishable from those of the algorithm in about one-tenth the time -- again, fast enough for real-time display. The system is a machine-learning system, meaning that it learns to perform tasks by analyzing training data; in this case, for each new task it learned, it was trained on thousands of pairs of images, raw and retouched. Each cell of the grid contains formulae that determine modifications of the color values of the source images. The full-resolution version of the HDR system took about 10 times as long to produce an image as the original algorithm, or 100 times as long as the researchers' system.
Singapore and MIT have been at the forefront of autonomous vehicle development. Now, leveraging similar technology, MIT and Singaporean researchers have developed and deployed a self-driving wheelchair at a hospital. Spearheaded by Daniela Rus, the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science and director of MIT's Computer Science and Artificial Intelligence Laboratory, this autonomous wheelchair is an extension of the self-driving scooter that launched at MIT last year -- and it is a testament to the success of the Singapore-MIT Alliance for Research and Technology, or SMART – a collaboration between researchers at MIT and in Singapore. Rus, who is also the principal investigator of the SMART Future Urban Mobility research group, says this newest innovation can help nurses focus more on patient care as they can get relief from logistics work which includes searching for wheelchairs and wheeling patients in the complex hospital network.