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This Funding Opportunity Announcement (FOA) intends to support a Brain Cell Data Center (BCDC) that will work with other BICCN Centers and interested researchers to establish a web-accessible information system to capture, store, analyze, curate, and display all data and metadata on brain cell types, and their connectivity. The BCDC is expected to: (1) lead the effort to establish spatial and semantic standards for managing heterogeneous brain cell census data types and information; (2) lead the effort to collect and register multimodal brain cell census data to common brain coordinate systems; (3) generate searchable 2D and 3D digital brain atlases for cell census data; and (4) generate a unified and comprehensive brain cell knowledge base that integrates all existing brain cell census data and information across diverse repositories.  A central goal of this and the three companion FOAs is to build a brain cell census resource that can be widely used throughout the research community.

This FOA solicits new theories, computational models, and statistical tools to derive understanding of brain function from complex neuroscience data. Proposed tools could include the creation of new theories, ideas, and conceptual frameworks to organize/unify data and infer general principles of brain function; new computational models to develop testable hypotheses and design/drive experiments; and new mathematical and statistical methods to support or refute a stated hypothesis about brain function, and/or assist in detecting dynamical features and patterns in complex brain data. It is expected that the tools developed under this FOA will be made widely available to the neuroscience research community for their use and modification. Investigative studies should be limited to validity testing of the tools being developed.

This funding opportunity announcement (FOA) invites applications for mentored career enhancement (K18) awards in research areas that are highly relevant to the NIH BRAIN Initiative. This career enhancement program will support development of research capability for the BRAIN Initiative, with specific emphasis on cross-training independent investigators in a substantively different area of neuroscience, neuroethics, or in a quantitative and physical discipline (e.g., physics, chemistry, engineering, computer science, mathematics); and vice versa, cross-training independent investigators trained in a quantitative or physical discipline proposing to gain in-depth training in a high-priority area of neuroscience. The research project conducted under this K18 should enhance the candidate's ability to significantly contribute to or lead projects that investigate questions central to the goals of the BRAIN Initiative. Eligible candidates are independent investigators at any faculty rank or level.

This funding opportunity announcement (FOA) invites applications for mentored career enhancement (K18) awards in research areas that are highly relevant to the NIH BRAIN Initiative. This career enhancement program will support development of research capability for the BRAIN Initiative, with specific emphasis on cross-training independent investigators in a substantively different area of neuroscience, neuroethics, or in a quantitative and physical discipline (e.g., physics, chemistry, engineering, computer science, mathematics); and vice versa, cross-training independent investigators trained in a quantitative or physical discipline proposing to gain in-depth training in a high-priority area of neuroscience. The research project conducted under this K18 should enhance the candidate's ability to significantly contribute to or lead projects that investigate questions central to the goals of the BRAIN Initiative. Eligible candidates are independent investigators at any faculty rank or level.

The purpose of this Funding Opportunity Announcement [FOA] submitted through the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative is to stimulate the development and validation of novel tools and analytical methods to target, identify and characterize non-neuronal cells in the brain. This FOA complements previous and ongoing cell-census and tool development efforts initiated under BRAIN, RFA-MH-14-215 and RFA-MH-14-216, that have focused almost exclusively on neuronal cells. The cutting-edge tools and methods developed under this opportunity should focus specifically on providing improved points of entry into non-neuronal cell-types (glial and vascular) to enable their inventory and characterization within the CNS and help define how these cells interact among each other and with neuronal cells to impact functional circuitries. Plans for validating the utility of the tool/technology/method and demonstrating its advantage over currently available approaches will be an essential feature of a successful application. Tools that can be used in several species or model organisms rather than in a single species are especially desirable.

This FOA solicits new theories, computational models, and statistical tools to derive understanding of brain function from complex neuroscience data. Proposed tools could include the creation of new theories, ideas, and conceptual frameworks to organize/unify data and infer general principles of brain function; new computational models to develop testable hypotheses and design/drive experiments; and new mathematical and statistical methods to support or refute a stated hypothesis about brain function, and/or assist in detecting dynamical features and patterns in complex brain data. It is expected that the tools developed under this FOA will be made widely available to the neuroscience research community for their use and modification. Investigative studies should be limited to validity testing of the tools being developed.

The purpose of this Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative is to encourage applications that will develop and validate tools and resources to facilitate the detailed analysis of brain microconnectivity. Novel and augmented techniques are sought that will ultimately be broadly accessible to the neuroscience community for the interrogation of microconnectivity in healthy and diseased brains of model organisms and humans. Development of technologies that will significantly drive down the cost of connectomics would enable routine mapping of the microconnectivity on the same individuals that have been analyzed physiologically, or to compare normal and pathological tissues in substantial numbers of multiple individuals to assess variability. Advancements in both electron microscopy (EM) and super resolution light microscopic approaches are sought. Applications that propose to develop approaches that break through existing technical barriers to substantially improve current capabilities are highly encouraged. Proof-of-principle demonstrations and/or reference datasets enabling future development are welcome, as are improved approaches for automated segmentation and analysis strategies of neuronal structures in EM images.

The purpose of the BRAIN Initiative Fellows (F32) program is to enhance the research training of promising postdoctorates, early in their postdoctoral training period, who have the potential to become productive investigators in research areas that will advance the goals of the BRAIN Initiative. Applications are encouraged in any research area that is aligned with the BRAIN Initiative, including neuroethics. Applicants are expected to propose research training in an area that complements their predoctoral research. Formal training in quantitative perspectives and analytical tools is expected to be an integral part of the proposed research training plan. In order to maximize the training potential of the F32 award, this program encourages applications from individuals who have not yet completed their terminal doctoral degree and who expect to do so within 12 months of the application due date. On the application due date, candidates may not have completed more than 6 months of postdoctoral training.  

These awards encourage and support scientists working on the development of novel and creative approaches to understanding brain function. The fund supports efforts to examine how a new technology may be used to monitor, manipulate, analyze, or model brain function at any level, from the molecular to the entire organism. Technology may take any form, from biochemical tools to instruments to software and mathematical approaches. Because the program seeks to advance and enlarge the range of technologies available to the neurosciences, research based primarily on existing techniques will not be considered.

These awards encourage and support scientists working on the development of novel and creative approaches to understanding brain function. The fund supports efforts to examine how a new technology may be used to monitor, manipulate, analyze, or model brain function at any level, from the molecular to the entire organism. Technology may take any form, from biochemical tools to instruments to software and mathematical approaches. Because the program seeks to advance and enlarge the range of technologies available to the neurosciences, research based primarily on existing techniques will not be considered.

This Funding Opportunity Announcement (FOA) solicits applications to develop web-accessible data archives to capture, store, and curate data related to BRAIN Initiative activities.  The data archives will work with the research community to incorporate tools that allow users to analyze and visualize the data, but the creation of such tools is not part of this FOA.  The data archives will use appropriate standards to describe the data, but the creation of such standards is not part of this FOA. A goal of this program is to advance research by creating a community resource data archive with appropriate standards and summary information that is broadly available and accessible to the research community for furthering research.

This Funding Opportunity Announcement (FOA) solicits applications to develop web-accessible data archives to capture, store, and curate data related to BRAIN Initiative activities.  The data archives will work with the research community to incorporate tools that allow users to analyze and visualize the data, but the creation of such tools is not part of this FOA.  The data archives will use appropriate standards to describe the data, but the creation of such standards is not part of this FOA. A goal of this program is to advance research by creating a community resource data archive with appropriate standards and summary information that is broadly available and accessible to the research community for furthering research.

Although invention and proof-of-concept testing of new technologies are a key component of the BRAIN Initiative, to achieve their potential these technologies must also be optimized through feedback from end-users in the context of the intended experimental use. This seeks applications for the optimization of existing and emerging technologies and approaches that have potential to address major challenges associated with recording and manipulating neural activity, at or near cellular resolution, at multiple spatial and temporal scales, in any region and throughout the entire depth of the brain. This FOA is intended for the iterative refinement of emergent technologies and approaches that have already demonstrated their transformative potential through initial proof-of-concept testing, and are appropriate for accelerated development of hardware and software while scaling manufacturing techniques towards sustainable, broad dissemination and user-friendly incorporation into regular neuroscience practice. Proposed technologies should be compatible with experiments in behaving animals, and should include advancements that enable or reduce major barriers to hypothesis-driven experiments. Technologies may engage diverse types of signaling beyond neuronal electrical activity for large-scale analysis, and may utilize any modality such as optical, electrical, magnetic, acoustic or genetic recording/manipulation. Applications that seek to integrate multiple approaches are encouraged. Applications are expected to integrate appropriate domains of expertise, including where appropriate biological, chemical and physical sciences, engineering, computational modeling and statistical analysis.

Understanding the dynamic activity of neural circuits is central to the NIH BRAIN Initiative. This FOA seeks applications for proof-of-concept testing and development of new technologies and novel approaches for largescale recording and manipulation of neural activity to enable transformative understanding of dynamic signaling in the nervous system. In particular, we seek exceptionally creative approaches to address major challenges associated with recording and manipulating neural activity, at or near cellular resolution, at multiple spatial and/or temporal scales, in any region and throughout the entire depth of the brain. It is expected that the proposed research may be high-risk, but if successful could profoundly change the course of neuroscience research. Proposed technologies should be compatible with experiments in behaving animals, and should include advancements that enable or reduce major barriers to hypothesis-driven experiments. Technologies may engage diverse types of signaling beyond neuronal electrical activity for large-scale analysis, and may utilize any modality such as optical, electrical, magnetic, acoustic or genetic recording/manipulation. Applications that seek to integrate multiple approaches are encouraged. Where appropriate, applications are expected to integrate appropriate domains of expertise, including biological, chemical and physical sciences, engineering, computational modeling and statistical analysis.

This Funding Opportunity Announcement (FOA) solicits applications to develop web-accessible data archives to capture, store, and curate data related to BRAIN Initiative activities. The data archives will work with the research community to incorporate tools that allow users to analyze and visualize the data, but the creation of such tools is not part of this FOA. The data archives will use appropriate standards to describe the data, but the creation of such standards is not part of this FOA. A goal of this program is to advance research by creating a community resource data archive with appropriate standards and summary information that is broadly available and accessible to the research community for furthering research.

Recent advances in communications, computation, and sensing technologies offer unprecedented opportunities for the design of cyber-physical systems with increased responsiveness, interconnectivity and automation. To meet new challenges and societal needs, the Energy, Power, Control and Networks (EPCN) Program invests in systems and control methods for analysis and design of cyber-physical systems to ensure stability, performance, robustness, and security. Topics of interest include modeling, optimization, learning, and control of networked multi-agent systems, higher-level decision making, and dynamic resource allocation as well as risk management in the presence of uncertainty, sub-system failures and stochastic disturbances. EPCN also invests in adaptive dynamic programing, brain-like networked architectures performing real-time learning, and neuromorphic engineering. EPCN supports innovative proposals dealing with systems research in such areas as energy, transportation, and nanotechnology. EPCN places emphasis on electric power systems, including generation, transmission, storage, and integration of renewables; power electronics and drives; battery management systems; hybrid and electric vehicles; and understanding of the interplay of power systems with associated regulatory and economic structures and with consumer behavior. Also of interest are interdependencies of power and energy systems with other critical infrastructures. Topics of interest also include systems analysis and design for energy scavenging and alternate energy technologies such as solar, wind, and hydrokinetic. The program also supports innovative tools and test beds, as well as curriculum development integrating research and education. In addition to single investigator projects, EPCN encourages cross-disciplinary proposals that benefit from active collaboration of researchers with complementary skills.

Recent advances in communications, computation, and sensing technologies offer unprecedented opportunities for the design of cyber-physical systems with increased responsiveness, interconnectivity and automation. To meet new challenges and societal needs, the Energy, Power, Control and Networks (EPCN) Program invests in systems and control methods for analysis and design of cyber-physical systems to ensure stability, performance, robustness, and security. Topics of interest include modeling, optimization, learning, and control of networked multi-agent systems, higher-level decision making, and dynamic resource allocation as well as risk management in the presence of uncertainty, sub-system failures and stochastic disturbances. EPCN also invests in adaptive dynamic programing, brain-like networked architectures performing real-time learning, and neuromorphic engineering. EPCN supports innovative proposals dealing with systems research in such areas as energy, transportation, and nanotechnology. EPCN places emphasis on electric power systems, including generation, transmission, storage, and integration of renewables; power electronics and drives; battery management systems; hybrid and electric vehicles; and understanding of the interplay of power systems with associated regulatory and economic structures and with consumer behavior. Also of interest are interdependencies of power and energy systems with other critical infrastructures. Topics of interest also include systems analysis and design for energy scavenging and alternate energy technologies such as solar, wind, and hydrokinetic. The program also supports innovative tools and test beds, as well as curriculum development integrating research and education. In addition to single investigator projects, EPCN encourages cross-disciplinary proposals that benefit from active collaboration of researchers with complementary skills.

The complexities of brain and behavior pose fundamental questions in many areas of science and engineering, drawing intense interest across a broad spectrum of disciplinary perspectives while eluding explanation by any one of them. Rapid advances within and across disciplines are leading to an increasingly interconnected fabric of theories, models, empirical methods and findings, and educational approaches, opening new opportunities to understand complex aspects of neural and cognitive systems through integrative multidisciplinary approaches. This program calls for innovative, integrative, boundary-crossing proposals that can best capture those opportunities. NSF seeks proposals that are bold, risky, and transcend the perspectives and approaches typical of single-discipline research efforts

The complexities of brain and behavior pose fundamental questions in many areas of science and engineering, drawing intense interest across a broad spectrum of disciplinary perspectives while eluding explanation by any one of them. Rapid advances within and across disciplines are leading to an increasingly interconnected fabric of theories, models, empirical methods and findings, and educational approaches, opening new opportunities to understand complex aspects of neural and cognitive systems through integrative multidisciplinary approaches.

A grand challenge in computing is the creation of machines that can proactively interpret and learn from data in real time, solve unfamiliar problems using what they have learned, and operate with the energy efficiency of the human brain. While complex machine-learning algorithms and advanced electronic hardware (henceforth referred to as 'hardware') that can support large-scale learning have been realized in recent years and support applications such as speech recognition and computer vision, emerging computing challenges require real-time learning, prediction, and automated decision-making in diverse domains such as autonomous vehicles, military applications,healthcare informatics and business analytics. A salient feature of these emerging domains is the large and continuously streaming data sets that these applications generate, which must be processed efficiently enough to support real-time learning and decision making based on these data. This challenge requires novel hardware techniques and machine-learning architectures.This solicitation seeks to lay the foundation for next-generation co-design of RTML algorithms and hardware, with the principal focus on developing novel hardware architectures and learning algorithms in which all stages of training (including incremental training, hyperparameter estimation, and deployment) can be performed in real time. The National Science Foundation (NSF) and the Defense Advanced Research Projects Agency (DARPA) are teaming up through this Real-Time Machine Learning (RTML) program to explore high-performance, energy-efficient hardware and machine-learning architectures that can learn from a continuous stream of new data in real time, through opportunities for post-award collaboration between researchers supported by DARPA and NSF.

A grand challenge in computing is the creation of machines that can proactively interpret and learn from data in real time, solve unfamiliar problems using what they have learned, and operate with the energy efficiency of the human brain. While complex machine-learning algorithms and advanced electronic hardware (henceforth referred to as 'hardware') that can support large-scale learning have been realized in recent years and support applications such as speech recognition and computer vision, emerging computing challenges require real-time learning, prediction, and automated decision-making in diverse domains such as autonomous vehicles, military applications, healthcare informatics and business analytics.

The Energy, Power, Control, andNetworks (EPCN) Program supports innovative research in modeling, optimization, learning, adaptation, and control of networked multi-agent systems, higher-level decision making, and dynamic resource allocation, as well as risk management in the presence of uncertainty, sub-system failures, and stochastic disturbances. EPCN also invests in novel machine learning algorithms and analysis, adaptive dynamic programming, brain-like networked architectures performing real-time learning, and neuromorphic engineering. EPCN’s goal is to encourage research on emerging technologies and applications including energy, transportation, robotics, and biomedical devices & systems. EPCN also emphasizes electric power systems, including generation, transmission, storage, and integration of renewable energy sources into the grid; power electronics and drives; battery management systems; hybrid and electric vehicles; and understanding of the interplay of power systems with associated regulatory & economic structures and with consumer behavior.

The Science of Learning program supports potentially transformative basic research to advance the science of learning. The goals of the SL Program are to develop basic theoretical insights and fundamental knowledge about learning principles, processes and constraints. Projects that are integrative and/or interdisciplinary may be especially valuable in moving basic understanding of learning forward but research with a single discipline or methodology is also appropriate if it addresses basic scientific questions in learning. The possibility of developing connections between proposed research and specific scientific, technological, educational, and workforce challenges will be considered as valuable broader impacts, but are not necessarily central to the intellectual merit of proposed research. The program will support research addressing learning in a wide range of domains at one or more levels of analysis including: molecular/cellular mechanisms; brain systems; cognitive affective, and behavioral processes; and social/cultural influences. The program supports a variety of methods including: experiments, field studies, surveys, secondary-data analyses, and modeling.