AFOSR - Information and Networks

The Information and Networks Team within the Engineering and Information Science Branch is organized to support many U.S. Air Force priority areas including autonomy, space situational awareness, and cyber security. The research programs within this team lead the discovery and development of foundational issues in mathematical, information and network oriented sciences. They are organized along three themes: Information, Decision Making, and Networks.

The information theme addresses the critical challenges faced by the U.S. Air Force which lie at the intersection of the ability to collect, mathematically analyze, and disseminate large quantities of information in a time critical fashion with assurances of operation and security.

Closely aligned with the mathematical analysis of information is the need for autonomous decision making. Research in this theme focuses on the discovery of mathematical laws, foundational scientific principles, and new, reliable and robust algorithms, which underlie intelligent, mixed human-machine decision-making to achieve accurate real-time projection of expertise and knowledge into and out of the battle space.

Information analysis and decision making rarely occur in the context of a single source. The networks theme addresses critical issues involving how the organization and interaction among large collections of information providers and consumers contributes to an understanding of the dynamics of complex information systems.

The Information and Networks (AFOSR/RTA2) Program Officers and topics are:

  • Computational Cognition and Machine Intelligence
  • Computational Mathematics
  • Dynamics and Control
  • Dynamic Data and Information Processing
  • Information Assurance and Cybersecurity
  • Optimization and Discrete Mathematics
  • Science of Information, Computation, Learning, and Fusion
  • Trust and Influence
  • Complex Networks
  • Cognitive and Computational Neuroscience

Our research areas of interest are described in detail below:


Program Description: This program supports innovative basic research on the fundamental principles and methodologies needed to enable intelligent machine behavior, particularly in support of mixed-initiative (i.e., human-machine teaming) systems. The overall vision of this program is that future computational systems will achieve high levels of performance, adaptation, flexibility, self-repair, and other forms of intelligent behavior in the complex, uncertain, adversarial, and highly dynamic environments faced by the U.S. Air Force. This program covers the full spectrum of computational and machine intelligence, from cognitively plausible reasoning processes that are responsible for human performance in complex problem-solving and decision-making tasks, to non-cognitive computational models of intelligence necessary to create robust intelligent systems. Robustness in this context is the ability to achieve high performance given at least some or all of the following factors: uncertainty, incompleteness or errors in knowledge; limitations on sensing; real-world complexity and dynamic change; adversarial factors; unexpected events including system faults; and out-of-scope requirements on system behavior. In the midst of this spectrum are the technologies explicitly needed to seamlessly incorporate intelligent computational systems into mixed human-machine teams. The program is divided into three sub-areas that span the full spectrum of computational and machine intelligence. They are: Computational Cognition, Human-Machine Teaming and Machine Intelligence.

The program encourages cross-disciplinary teams with collaboration including computer scientists, neuroscientists, cognitive scientists, mathematicians, statisticians, operation and management science researchers, information scientists, econometricians and game theoreticians, etc., especially when the research pertains to common issues and when collaboration is likely to generate bidirectional benefits. This program is aggressive, accepts risk, and seeks to be a pathfinder for U.S. Air Force research in this area. Proposals that may lead to breakthroughs or highly disruptive results are especially encouraged.

Basic Research Objectives: The Computational Cognition sub-area supports innovative basic research on high-order cognitive processes that are responsible for good human performance in complex problem solving and decision-making tasks – we only want to model the things people excel at. The sub-area also seeks to support research on building computational systems that derive from and/or integrate cognitive and biological models of human and animal intelligence. The overall objective is to understand and exploit these processes to create computational models that perform as well as or better than the reasoning systems they emulate. This sub-area seeks basic research that pertains to exploiting the capabilities of the mind and brain (human or animal) for creating more intelligent machines, as well as cognitively plausible mechanisms inspired by human (or animal) reasoning. This includes computational models based on human and animal performance in perception, attention, memory, learning, reasoning, and decision making in order to improve machine performance.

This sub-area does NOT, however, support statistical approaches to machine learning (e.g., “Deep Learning”), or related variants, as fundamental science in that area is addressed by the Science of Information, Computation, Fusion and Learning program described elsewhere in this BAA.

The Machine Intelligence sub-area supports innovative basic research on fundamental principles and methodologies of computational intelligence necessary to create robust intelligent systems. These methodologies may be cognitively inspired, or non-cognitive in nature, taking full advantage of the strengths embodied in mathematical and computational systems, such as the ability to reason with complex formal logic. This sub-area encourages research enabling the creation of computational systems that embody intelligent behavior based on cognitively inspired or purely mathematical approaches. Proposals that lead to advances in the basic principles of machine intelligence for memory, reasoning, planning, scheduling, and cognitively-inspired learning (i.e., NOT “Deep Learning” or other statistical means), action, and communication are desired insofar as these contribute directly towards robustness as defined above.

The Human-Machine Teaming sub-area is primarily concerned with the machine-side of mixed human-machine decision-making, which appears at all levels of U.S. Air Force operations and pervades every stage of U.S. Air Force missions. To that end, new theoretical and empirical guidance is needed to prescribe maximally effective mixtures of human and machine decision making in environments that are becoming increasingly complex and demanding as a result of the high uncertainty, complexity, time urgency, and rapidly changing nature of military missions. This sub-area seeks new empirical and theoretical basic research that enables intelligent machines to perform as true “teammates,” adapting their behavior to accommodate changes in the environment, as well as augmenting the performance of human teammates when needed. This includes basic science in collaborative human-machine teams to aid the machine-side of inference, analysis, prediction, planning, scheduling, and decision making.

You are highly encouraged to contact our Program Officer prior to developing a full proposal to briefly discuss the current state-of-the-art, how your research would advance it, the approximate cost for a three (3) to five (5) year effort, and if there are any specific submission target dates.


Program Description: This program seeks to develop innovative mathematical methods and fast, reliable and scalable algorithms aimed at making radical advances in computational science and large-scale engineering and design. Research in computational mathematics underpins the fundamental understanding of complex physical phenomena and leads to predictive simulation capabilities that are crucial to the design and control of future U.S. Air Force systems, and their lifetime expectancy. Proposals to this program should focus on fundamental scientific and mathematical innovations, and should have the potential to address some of the most important computational challenges in science and engineering. Additionally, it is desirable to frame the basic research ideas in the context of applications relevant to the U.S. Air Force, which can serve simultaneously to focus the research and to provide avenues for transition of basic research outcomes into practice. Applications of current Air Force interest include, but are not limited to, quantum physics and quantum information systems, plasma dynamics, turbulence (e.g., in fluids, combustion, plasma), lasers and directed energy, aero-thermo-dynamics, information science, data analysis (including machine learning), biophysics, and material and structural sciences.

Basic Research Objectives: Research under this program has traditionally emphasized schemes that address the discretization and numerical solution of complex systems of equations, generally partial differential equations derived from physical models. However, alternative computational approaches are of keen interest, particularly in connection with emerging and multidisciplinary applications.

Increased emphasis in this portfolio is placed on approaches that can handle a very high number of dimensions, uncertainty and stochasticity for non-Markovian processes, far from equilibrium conditions, and/or a wide range of scales (space, time, physical parameters, or complexity). Research areas of particular interest currently include:

  • Mathematical methods for complexity reduction of high-dimensional, non- linear and multiscale problems, e.g., via projection-based methods and/or new machine-learning concepts. Such systems may have continuous, discrete or mixed representations, and may reside on graphs with evolving topology.
  • Mathematical approaches to the modeling of non-equilibrium statistical processes and turbulent dynamics with multiple physical interactions and large parameter spaces; of special interest are methods which effectively allow bi-directional transfer of information across scales, and can simultaneously reduce the computational burden while preserving the correct physics of interaction, including conservation laws and instability regimes.
  • Highly efficient and accurate methods for high-dimensional, nonlinear and stochastic dynamics with constraints. In particular, we are seeking revolutionary approaches to solving Hamilton-Jacobi-Bellman equations, optimal transport problems, and inverse problems for highly complex conditions. Of particular interest are applications in large-scale game theory, self-organized criticality and cascades, and the prediction of rare and extreme events.
  • Traditional computational methods involving high-order spatial and temporal algorithms remain of interest, if they have the potential for significant breakthrough and are able to meet the formidable computational challenges associated with current and future engineering problems of interest to the U.S. Air Force.

The list above is not exhaustive and other approaches can be suggested to the Program Officer, who can then determine if a proposal is warranted and of potential interest. All proposed methods must be innovative, have quantifiable measures of fidelity, efficiency and adaptively, must be based on rigorous analysis and preferably demonstrated on canonical challenge and grand challenge problems.

You are encouraged to contact our Program Officer prior to developing a full proposal to briefly discuss the current state-of-the-art, how your research would advance it, the approximate cost for a three (3) to five (5) year effort, and if there are any specific submission target dates.


Program Description:
This program emphasizes the interplay of dynamical systems and control theories, with the aim of developing innovative synergistic strategies for the design and analysis of controlled systems that enable radically enhanced capabilities, including performance and operational efficiency for future U.S. Air Force systems. Proposals should focus on the fundamental science and mathematics, while having relevance to future Air Force systems and operations. These applications currently include information systems, as well as autonomous/semi-autonomous aerial vehicles, munitions, and space vehicles. Note that research specifically aimed at an Air Force application may not be considered fundamental research; therefore, future Air Force applications should be mentioned as examples or possibly motivations for fundamental theory in dynamical systems and control theory.

The dramatic increase in complexity of Air Force systems provides unique challenges for the Dynamics and Control Program. Meeting these challenges may require interdisciplinary approaches as well as deeper studies within single disciplines.

Lastly, note that the Dynamics and Control Program places special emphasis on mathematically rigorous techniques addressing realistic treatment of applications, complexity management, semi-autonomous systems, and real-time operation in stochastic and adversarial environments.

Basic Research Objectives: Current research interests include: methods of dynamical analysis of complex systems for the purpose of real-time control; control of ensemble and infinite dimensional systems; deterministic time and/or real-time reachability and viability set calculation and verification and validation of hybrid systems with formal specifications; distributed and decentralized decision making and control for coordinated autonomous/semi-autonomous aerospace vehicles with realistic nodal dynamics (e.g., not linear consensus or linear estimation over graphs), considering constraints, uncertain, information rich, dynamically changing, networked environments with time-varying topologies; novel schemes that enable challenging multi-agent aerospace tracking in complex, cluttered scenarios; robust and adaptive non-equilibrium (e.g., set-based) control of nonlinear processes where the primary objective is enhanced operability rather than just local stability; new methods for understanding and mitigating the effects of uncertainties in dynamical processes where uncertainty distribution is non-Gaussian; novel theory for control of hybrid systems that can intelligently manage actuator, sensor, and processor communications in a complex, spatially distributed and evolving system of systems; sensor rich, data driven adaptive control; and applying control concepts motivated by studies of biological systems.

In general, the control of large complex, multi-scale, hybrid, highly uncertain nonlinear systems is of increasing interest. Furthermore, new mathematics in clear support of dynamics and control is of fundamental importance to this portfolio. In this regard, some areas of interest include, but are not limited to, hybrid dynamical systems theory, geometric and algebraic methods of dynamics and control, stochastic and adversarial systems, control of cyber physical systems with formal specifications, emerging areas of control theory, graph theoretic control theory over nonlinear dynamics at nodes of graphs, partial and corrupted information, nonlinear control and estimation, and novel computational techniques specifically aimed at control of systems with large data.

You are highly encouraged to contact our Program Officer prior to developing a full proposal to briefly discuss the current state-of-the-art, how your research would advance it, and the approximate cost for a three (3) to five (5) year effort.


Program Description:
The portfolio seeks to extend the Dynamic Data Driven Applications Systems (DDDAS) framework with emphasis on the mathematical concepts that dynamically incorporate additional data, whether measured or from models, into an executing application, and in reverse, the ability to steer measurement collections, model refinements, and system awareness. Key developments should harness the use of first-principle models towards signals, data, and information processing substantial comparative improvements. The portfolio encourages multidisciplinary research, especially synergistic and systematic collaborations between domain researchers in mathematics and statistics, computer sciences, and the design and implementation of measurement and control systems for modeling, diagnostics, and analytics.

Basic Research Objectives: Foster individual and multidisciplinary research, technology development, and system analysis over emerging science and technology frontiers.

Domain modeling: Methods leveraging large-scale simulations for real-time control, in concert with heterogeneous data collection, model updates, and system processing. Research advances should describe different levels of detail and modalities, invoke appropriate models, and interface with other data systems. For example, ideas to engender an integration of large-scale simulations, models, and data to advance traditional data processing paradigms.

Mathematical and Statistical Algorithms: Design methods for stable and robust convergence properties under perturbations induced by time-dependent (periodic and non-periodic, scheduled and event-driven) data inputs, multiple scales and model variations. Address enhanced asynchronous algorithms with stable communication between networked resources, multimodal modeling, and uncertainty quantification. For example, concepts to dynamically invoke models requiring elegant methods of uncertainty quantification, management, and propagation.

Measurement Systems and Methods: Innovate instrumentation platforms for collecting data, registering measurements, controlling sampling rates, and multiplexing multisource information. For example, designs to determine heterogeneous and embedded distributed sensor networks architectures, information fusion paradigms, and operationally robust performance.

Areas of interest to the Air Force include (a) autonomy (e.g., leveraging large-scale modeling of mission planning, collaborative/cooperative control, and data learning for data analytics); (b) agility (e.g., designing computational methods of sensor-based processing, ad-hoc network configurations, and multi-scale multi-physics simulations for decision support); (c) authority (e.g., coupling high-performance aircraft health monitoring, space situational awareness, and ground operations for command and control); and (d) robustness (e.g., understanding materials stresses and degradation; embedded diagnostics, complex adaptive systems verification and validation, and cognitive performance augmentation for situational understanding).

New approaches are encouraged for combining computational, empirical, cognitive, theoretical, and analytical methods for interactive testing of multiple scientific and engineering hypotheses. Programmatic activities launched under this initiative will support research in individual areas of interest to the Air Force Research Laboratory, but mostly in the context of multidisciplinary research across the Basic Area Objectives mentioned above.

You are highly encouraged to contact the Program Officer prior to developing a full proposal to discuss the current state-of-the-art, how your research would advance it, the approximate cost for a two (2) to three (3) year effort, and if there are any specific submission target dates.


Program Description:
Securing cyberspace, i.e. defending against and preventing cyber-attacks are not new challenges but these have become increasingly pressing in the light of technological advancements. Software and protocols are continuously becoming more complex to meet application demands. More flexible computing environments, such as distributed systems, demand new ways of thinking how to ensure secure end-to-end functionalities, even though components are only known to be individually secure. The emergence of nanoscale devices and quantum information processing and communication also portends new technological challenges for cybersecurity. By the same token, these new technologies potentially offer unparalleled security solutions to the existing or future problems.

Although engineering practices continue to provide short-term and temporary relieves to these pressing needs, new scientific ideas are required to address the lack of security and the explosive growth of hostile actions in cyberspace, especially taking into account of emerging technologies. Many fundamental concepts are still eluding precise formulation and awaiting rigorous responses. The goal of this Basic Research program is to explore novel, promising concepts and methodologies that can establish a firm scientific foundation for cybersecurity and potentially tackle the difficult technical hurdles described above.

Basic Research Objectives: Recent developments and advances in the following research areas of computer science and mathematics are expected to provide valuable insights into various cybersecurity problems: dependent type theory, cryptographic protocols for interactive computation and communication, interactive and automated theorem proving, language-based techniques in software and hardware for formal specification and verification, secure protocols, game theory with strong security content, obfuscation and fully homomorphic encryption, model categories, formalized mathematics. Broadly speaking, cross-fertilization of mathematical formalisms and logical constructs will likely continue to play a central role in the construction and verification of security invariants, and in the study of security models or security principles.

These scientific advances are expected to contribute fresh ideas to a number of fundamental cybersecurity topics: composition of security properties and protocols in distributed interactive systems without the need of trusted third parties; rigorous techniques to enable persistent and secure operations on unsecure or untrusted systems; information flow security and non-interference in dynamic and distributed settings; new security invariants that can readily be computed and interpreted, especially for systems endowed with rich geometric dynamics; rigorous proofs and construction of obfuscation techniques for programs and circuits to enhance security; formal verification and certification of the correctness of complex large-scale mathematical proofs and critical computer systems.

Aside from software and secure protocols, nanoscale material properties and quantum effects should offer added security capabilities for future computing devices that cannot be realized by today’s technologies. They potentially enable physical construction of cryptographic primitives that are traditionally described by algorithms and typically implemented by software. Random Number Generators and Physical Unclonable Functions are simplest examples of such construction. At the same time, securing future unconventional technologies will require the introduction of new security principles and security models that may substantially deviate from the traditional approaches. In fact, various concepts in quantum information science and quantum computation such as quantum resources (entanglement, non-locality, contextuality, etc.) and quantum computational/communication complexity are highly relevant to the security of future communication and computing systems in which classical and quantum devices interact.

Research areas of interest to this program include, but are not limited to, the methodologies and topics described above. Highest priority will be given to projects with novel scientific ideas that potentially deliver new DoD/Air Force capabilities.

You are highly encouraged to contact the Program Officer prior to developing a full proposal to briefly discuss the current state-of-the-art, how your research would advance it, the approximate cost for a three (3) to five (5) year effort, and if there are any specific submission target dates.


Program Description:
The program goal is the development of mathematical methods for the optimization of large and complex models that will address future decision problems of interest to the U.S. Air Force. Areas of fundamental interest include resource allocation, planning, logistics, engineering design, and scheduling. Increasingly, the decision models will address problems that arise in the design, management and defense of complex networks, in robust decision making, in performance, operational efficiency, and optimal control of dynamical systems, and in artificial intelligence and information technology applications.

Basic Research Objectives: There will be a focus on the development of new nonlinear, integer, and combinatorial optimization algorithms, including those with stochastic components. Techniques designed to handle data that are uncertain, evolving, incomplete, conflicting, or overlapping are particularly important.

As basic research aimed at having the broadest possible impact, the development of new computational methods will include an emphasis on theoretical underpinnings, on rigorous convergence analysis, and on establishing provable bounds for (meta-) heuristics and other approximation methods.

You are highly encouraged to contact our Program Officer prior to developing a full proposal to briefly discuss the current state-of-the-art, how your research would advance it, and the approximate cost for a three (3) to five (5) year effort, and if there are any specific submission target dates.


Program Description:
The U.S. Air Force collects vast amounts of data through various modes at various times in order to extract and derive needed “information” from these large and heterogeneous (mixed types) data sets. Some data, such as those collected from magnetometers, register limited information content which is more identifiable at the sensor level but beyond human’s sensory reception. Other types of data, such as video cameras or text reports, possess more semantic information that is closer to human cognition and understanding. Nevertheless, these are instances of disparate data which encapsulate different types of “information” pertained to, perhaps, the same event(s) captured by different modalities through sensing and collection.

In order to understand and interpret information contained in various data sources, it is necessary to extract relevant pieces of information from these datasets and to make inferences based on prior knowledge and probabilities. This bottom-up processing direction needs conceptually driven reasoning to integrate or fuse the previously extracted snippets of information by leveraging domain knowledge. Furthermore, the top-down processes can offer causal explanation or causal inference, generate new hypotheses, verify or test hypotheses in light of observed datasets. Between the data- driven and conceptually-driven ends, there may reside different levels of abstraction in which information is partially extracted and aggregated based on the nature of applications.

Basic Research Objectives: With the rationale and guiding principles outlined in the above paragraph, this program seeks fundamental research that potentially leads to scientific advancements in informatics, computation, and learning that can support processing and making sense of complex disparate information sources. After all, information processing can formally and fundamentally be described as computing and reasoning on various knowledge representations. Successes in addressing the research sub-areas stated below would give the U.S. Air Force new capabilities to: (1) shift emphasis from sensing to information awareness; (2) understand the underpinning of autonomy; (3) relieve human’s cognitive overload in dealing with the data deluge problem; (4) enhance human-machine interface in information processing.

To accomplish the research objectives, this program focuses on, but is not limited to, new techniques in mathematics, computing science, statistics and logic which have potentials to: (1) cope with various complex disparate data/information types; (2) integrate a diversity of unique reasoning and learning components collaborating simultaneously (e.g., multi-strategy reasoning and learning); (3) bridge correlational with causal discovery; (4) determine solutions or obstructions to local-to-global data- fusion problems; (5) mechanize reasoning/learning and computing in the same computational environment; (6) yield provably efficient procedures to enable or facilitate data analytics; (7) deal with high-dimensional and massive datasets with provably guaranteed performance.

You are highly encouraged to contact our Program Officer prior to developing a full proposal to briefly discuss the current state-of-the-art, how your research would advance it, and the approximate cost for a three (3) to five (5) year effort, and if there are any specific submission target dates.


Program Description:
The Trust and Influence program funds interdisciplinary high risk, transformative basic research that (1) elucidates the social and cognitive principles and processes surrounding the establishment, maintenance, and repair of trust between and among humans and intelligent agents, machines, algorithms, and/or other emergent technologies, with particular interest in situations where these concepts apply to heterogeneous, distributed teams or teaming constellations (i.e. teams of teams); and (2) advances the science of social influence to enhance understanding of hat consider how the phenomena and/or associated processes shape or affect human beliefs, perceptions, attitudes, and/or behaviors. The program encourages multidisciplinary and transdisciplinary approaches, which may include contributions from sociology, anthropology, computer and information science, psychology, cognitive science, linguistics, mathematics, economics, computational social science, and other social or behavioral sciences, among other disciplines. It further encourages research designs that utilize laboratory studies, modeling, and/or field research intended to develop novel, transformative theories, frameworks, or evaluative measures.

Basic Research Objectives: The research interests under this program can be defined broadly by three areas: trust in autonomous systems, socio-digital influence, and computational methods in social science. In the area of trust in autonomous systems there is particular interest in (1) empirical studies to examine drivers of trust between humans and intelligent, autonomous or robotic agents, (2) laboratory and field studies to examine the impact of socially-designed cues or physical features such as appearance, voice, personality, and other social elements on human trust and system performance, (3) development of trust metrics and other relevant constructs in human-machine teaming with a particular focus on real-time and dynamic assessment, and (4) modeling of human-machine teaming that supports adaptive and continuous improvement of joint performance in complex environments. In the area of socio-digital influence, research is needed towards understanding how social and digital media are used to influence populations, spread ideas and change beliefs. The portfolio is concerned with behavioral effects, but also the cognitive processes that give rise to behavior and the neural underpinnings of those cognitive processes. There is a need for (1) laboratory and field studies to reveal sources of influence and persuasion in social media and across different cultural groups, (2) social, cognitive, and neural mechanisms of influence and persuasion (3) modeling and measuring the relationship between online and real-world behaviors, and (4) empirical studies to discover new theories of influence as it pertains to the cyber domain.

You are encouraged to contact our Program Officer prior to developing a full proposal to discuss alignment of your ideas with our program goals, your proposed methods, the scope of your proposed effort, and if there are any specific submission target dates.


Program Description:
Complex networks are pervasive in military, commercial, and civilian operations. Complex networks consist of a graph (directed or undirected) together with a set of attributes. These attributes can include scalar or multi-dimensional weights on the edges or nodes of the graph, topological characteristics of the graph, and processes that define the dynamics of the graph. Complex networks cut across many scientific disciplines (e.g., mathematics, computer science, engineering, socio-economics, biology, etc.) and many application domains (e.g., logistics, sensing, information systems). Networks fundamentally describe the structural aspects of interactions between individual agents. Networks can be extremely large and can have multiple characteristic scales. They can be static or dynamic. They can be physical or virtual. Networks can consist of multiple heterogeneous subnetworks (i.e., a network-of-networks), with explicit and implicit interdependencies. For example, logistical networks are intimately coupled to computer and electrical-power networks. Thus, the failure of a critical node or arc in one network can trigger failures in another, which can create a cascade event with catastrophic consequences. All of these characteristics of networks can make the analysis, understanding, and utilization of networks difficult and computationally prohibitive.

This basic-research program is focused on developing fundamental mathematical and algorithmic techniques to study, understand, analyze, and design complex networks and the dynamical processes coupled to the network characteristics. The program seeks innovative approaches with far-reaching potential, meaning that, ideally, the approaches should be applicable to broad classes of problems and not tied to a particular application domain. The networks of interest can have arbitrary topologies, can be static or dynamic, and can be subject to uncertain conditions, ranging from a stochastic environment to deliberate adversarial actions affecting both nodes and links.

Basic Research Objectives: This program is aimed at developing mathematical and algorithmic tools for the design, analysis, understanding, and utilization of networks. Problems of interest include, but are not limited to: (1) Algorithmic analysis of networks. This includes algorithmic techniques for solving important classes of problems on networks, such as optimal resource allocation on networks, interdiction and resiliency of networks, inference problems on networks including making global inferences from local information, and information flow on networks. The algorithms can be exact or approximate with performance guarantees. They can decentralized, randomized, and/or online if appropriate to the problem class. (2) Complexity reduction of networks. This includes techniques for decomposition, sparsification, aggregation, and dimensionality reduction of networks with the goal of making networks easier to understand, analyze, and visualize; (3) Extending network-analysis techniques to more general combinatorial, algebraic, and analytical structures, such as matrix-defined problems, submodular functions, and networks over function spaces.

Approaches may draw upon, but are not limited to, techniques from graph theory, optimization, combinatorics, algorithmic game theory, analysis, linear algebra, statistics, and probability.

You are highly encouraged to contact the Program Officer, preferably by email, prior to developing a full proposal, to briefly discuss the current state-of-the-art, how your research would advance it, the approximate cost, and if there are any specific submission target dates.


Program Description:
The Cognitive and Computational Neuroscience program funds high-risk, high-potential basic research that uses experimental and computational modeling techniques from systems neuroscience, cognitive neuroscience, computational/theoretical neuroscience, cognitive science, and cognitive psychology to understand the neural mechanisms responsible for perception, cognition, and behavior. The program also supports brain-inspired algorithm and hardware development provided these are useful for testing proposed neuroscience theories and/or enabling novel capabilities in computing, artificial intelligence, or autonomous systems.

Basic Research Objectives: Potential topics that would be of interest to the program include but are not limited to the following:

  • Neural Information Representation. It is well established that neurons communicate information via action potentials (APs, “spikes”), but understanding how neurons represent information has remained a long-standing challenge. Debates continue regarding whether neurons use precise spike timing or frequency to encode information, the functions of noisy, probabilistic population codes, and whether every spike carries signal. Proposed research projects should characterize neural activity with the aim of reliably decoding neuronal information. Proposers should describe how their research, if successful, will advance our ability to test hypotheses regarding neural mechanisms and functions and/or enable greater sophistication for applications such as sensory and sensorimotor prosthetics, brain-machine interfaces, and deception detection.
  • Bio-inspired Sensing. Humans and other animals often use strategies to process sensory information, recognize and locate objects, and navigate in real-world environments that outperform current man-made systems. Artificial systems featuring such strategies could augment human capabilities while enhancing or maintaining situational awareness. Proposals should describe a species’ sensory function in which the sensor, environment, and behavior align with the proposed application(s), hypotheses about the associated neural information processing mechanisms, and a research plan that culminates in a proof-of-concept demonstration.
  • Brain-inspired Machine Learning. Current machine learning algorithms excel at identifying statistical features in complex data sets, yet computers lack the robustness and generalizability associated with human learning. Specifically, humans can leverage previously learned knowledge to avoid needing large training sets, transfer knowledge from one task to other related tasks or contexts to accelerate learning, continuously update their learned knowledge, and adapt to time-varying contexts and environments. The program seeks to create brain-inspired or biomimetic algorithms that advance the state of the art and have the potential for revolutionary progress on these or related research challenges.
  • Brain-inspired Computing. Recent examples of commercially-developed neuromorphic hardware have focused on power consumption and density advantages over conventional hardware, but brain-inspired circuits also hold promise for novel computing architectures that can address problems that traditional von Neumann architectures cannot (or at least not within polynomial time or better). Research proposals on brain-inspired computational capabilities should describe how the research will fill gaps in computing or how it will enable novel artificial intelligence algorithms.

Innovative ideas that may not fit into these categories but are germane to the program’s general objectives are also welcomed.

Models of brain structures and functions should be biologically plausible and demonstrate consistency with the current understanding of the structure and function of the brain components being modeled. Brain-inspired algorithms or hardware need not remain entirely faithful to the biological details where other approaches are more reasonable, but neuroscience evidence should be used for benchmarking and theoretical guidance. Proposals should provide a definition of success based on specific metrics and/or one or more challenge problems.

Prospective proposers are highly encouraged to contact the Program Officer prior to developing a full proposal to discuss the proposed research and how it will advance the state of the art, the approximate cost for a three (3) to five (5) year effort, and any specific submission target dates.