Boston University – As part of its BRAIN initiative, the National Science Foundation announced a second series of grants this week awarded to multidisciplinary teams to further explore cognitive and neural systems, including “data-intensive neuroscience and cognitive science.”
NSF said Thursday (Aug. 18) the 18 grants totaling about $17 million were awarded to U.S. university research teams. The grants are worth up to $1 million each and would extend between two to four years.
“We expect new advances in theory and methods, technological innovations, educational approaches, research infrastructure and workforce development,” noted Betty Tuller, the NSF program director overseeing the awards.
Among the projects included in the second round of NSF funding are neuromorphic computing, memory optimization and spatial navigation investigations. The hope is that basic research across multiple disciplines will deliver more bang for the research buck by contributing, for example, to computational modeling as well as education research, NSF officials stressed.
BRAIN, which stands for Brain Research through Advancing Innovative Neurotechnologies, is an overarching NSF research effort aimed at accelerating development of new “neurotechnologies” via a multidisciplinary approach.
“The complexities of brain and behavior pose fundamental questions in many areas of science and engineering,” Kenneth Whang, an NSF program director, noted in a statement announcing the grant awards. “The mysteries of the brain draw intense interest across a broad spectrum of disciplinary perspectives yet elude explanation by any one of them, which is why team-based approaches are so necessary.”
Among the 18 new projects funded by NSF (the agency awarded a first round of brain research grants in fiscal 2015) is an effort by University of Tennessee researchers to develop “biometric membrane networks” that could be used as “adaptable neuromorphic computation circuits.”
According to the NSF award, the brain circuit research “seeks to advance computational strategies past Exascale, thus setting the stage for the next generation of low-power autonomic computation, distributed sensing and information storage.”
Meanwhile, a University of Washington project funded by NSF will explore the fundamentals of neural processing using data-intensive approaches. The expected outcome is a set of neural decoding algorithms that would be applicable to current brain-computer interface technologies, NSF said.
The neural network and circuit theme also is reflected in a grant to researchers from the University of Chicago and New York University. The project would combine multi-channel neurophysical recordings and neural circuit modeling. “This approach offers a potentially powerful data analysis tool and conceptualization of neural circuit computation in terms of neural population trajectories in a high-dimensional state space,” program officials said.
The NSF projects are collectively probing how a “three-pound multitasking marvel” operates. “In fractions of a second, all these different parts of the brain have to coordinate their activity,” explains Nancy Kopell, a mathematic professor at Boston University. “We’re just starting to understand how that kind of coordination can take place.”