NSF funds new integrative approaches to study the brain
The National Science Foundation (NSF) has awarded 18 grants to multidisciplinary teams from across the United States to conduct frontier research focused on neural and cognitive systems. Each award provides a research team with up to $1 million over two to four years.
The awards fall within four research themes:
- Neuroengineering and brain-inspired concepts and designs.
- Individuality and variation.
- Cognitive and neural processes in realistic, complex environments.
- Data-intensive neuroscience and cognitive science.
The first two themes were included in a first round of brain research funding in Fiscal Year 2015, when NSF announced 16 awards.
"Within each theme — and as a collective — we expect new advances in theory and methods, technological innovations, educational approaches, research infrastructure, and workforce development," said Betty Tuller, NSF program director in the Social, Behavioral and Economic Sciences Directorate, who will help oversee the awards.
In addition to the Social, Behavioral and Economic Sciences, three other NSF directorates will collaboratively support the awards: Computer & Information Science & Engineering; Education and Human Resources; and Engineering.
The projects will explore fundamental scientific and engineering queries, ranging from spatial navigation and memory optimization to neuromorphic computation. They are based on integrative strategies designed to transcend conventional perspectives and approaches, building on leading-edge research across multiple disciplines.
"Each project makes a scientific advance in multiple ways — for example, contributing to computational modeling research as well as education research," said Evan Heit, division director in NSF's Education and Human Resources Directorate.
Complexities of brain and behavior
The 18 newly funded projects stem from the cross-disciplinary NSF Integrative Strategies for Understanding Neural and Cognitive Systems program, which supports bold efforts to go beyond single-discipline research efforts in order to advance brain science. The awards will contribute to NSF's significant investments in support of the BRAIN Initiative, a coordinated research effort that seeks to accelerate the development of new neurotechnologies.
"The complexities of brain and behavior pose fundamental questions in many areas of science and engineering," said Kenneth Whang, NSF program director in the Computer & Information Science & Engineering Directorate. "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."
Alexander Leonessa, NSF program director in the Engineering Directorate, agreed on the value of the team-based approach.
"By encouraging collaborations among investigators from different disciplines, we were able to fund innovative, integrative, boundary-crossing proposals that can best capture the spirit of this opportunity," Leonessa said.
In addition, the program awarded supplemental funds of up to $200,000 each to 12 projects to connect basic research in computing, engineering and education to new challenges in neuroscience and cognitive science.
The 18 newly awarded projects are led by:
- Danielle Bassett of the University of Pennsylvania and Fabio Pasqualetti of the University of California, Riverside, A mechanistic model of cognitive control.
- Bingni Brunton of the University of Washington, Understanding neural processing in long-term, naturalistic human brain recordings using data-intensive approaches.
- Fow-Sen Choa of the University of Maryland, Baltimore County and Mary Kay Lobo of the University of Maryland, Baltimore, Focused electrical stimulator for targeted neuromodulation.
- Jochen Ditterich of the University of California, Davis, Probing the functional significance of brain oscillations through closed-loop phase-locked stimulation.
- Arne Ekstrom of the University of California, Davis, The neural basis of human spatial navigation in large-scale virtual spaces with vestibular input.
- David Freedman of the University of Chicago and Xiao-Jing Wang of New York University, Flexible rule-based categorization in neural circuits and neural network models.
- Emily Gibson of the University of Colorado Denver and Juliet Gopinath of the University of Colorado Boulder, Rebuilding neural pathway function using miniature integrated optics for neuron-level readout and feedback.
- Todd Gureckis of New York University, Using computational cognitive neuroscience to predict and optimize memory.
- Erin Hecht of Georgia State University, Individual variation, plasticity and learning in human brain evolution.
- Marc Howard of the Trustees of Boston University and Per Sederberg of Ohio State University, Learning efficient visual representations from realistic environments across time scales.
- Bruce McNaughton of the University of California, Irvine and Mark Reimers of Michigan State University, Hippocampal-cortical communication and the extraction of knowledge from memory.
- Seung-Jun Kim of the University of Maryland, Baltimore County and Vince Calhoun of The Mind Research Network, Flexible large-scale brain imaging analysis: diversity, individuality and scalability.
- Angela Laird of Florida International University, Integrative knowledge modeling in cognitive neuroimaging.
- Michael Mozer of the University of Colorado Boulder, Richard Baraniuk of Rice University and Harold Pashler of the University of California, San Diego, Operationalizing students' textbooks annotations to improve comprehension and long-term retention.
- Zhenpeng Qin of the University of Texas at Dallas, Sub-millisecond optically triggered compound release to study real-time brain activity and behavior.
- Matthew Reidenbach of the University of Virginia, and Barry Ache and Jose Principe of the University of Florida, A computational neuroscience framework for olfactory scene analysis within complex fluid environments.
- Garrett Rose of the University of Tennessee, Knoxville, Biomimetic membrane networks as adaptable neuromorphic computation circuits.
- Kimberly Turner of the University of California, Santa Barbara, μHammer: Impacting neuroscience one cell at a time.
To learn more about NSF investments in fundamental brain research, visit NSF.gov/brain.