Please find below the latest information about the National Science Foundation’s FY22/23 Emerging Frontiers in Research and Innovation (EFRI) Program topic: Brain-inspired Dynamics for Engineering Energy-Efficient Circuits and Artificial Intelligence (BRAID). This letter advertises the second and final call for applications. Due to BRAID’s core requirement of theoretical expertise in brain function, we strongly encourage participation from interested members of the theoretical and computational neuroscience research communities.
BRAID supports interdisciplinary research with the aim of fostering new engineering-driven scientific approaches to the design and development of brain-inspired engineered learning systems. The recent period of growth and excitement in neuroscience has provided many new insights and ways to understand the complexity of biological learning processes. This surge of neural data, methods, and theories has emphasized the critical interplay between spatial and temporal dynamics across time scales from milliseconds to lifetimes and spatial scales from subcellular structures to macroscale brain systems. However, the sources of the unparalleled learning efficiency supported by brain mechanisms across many species have remained unclear, particularly in terms of energetic and data-sampling requirements. BRAID seeks to exploit these emerging advances for the design of engineered learning systems that exhibit the flexibility, robustness, and efficiency of biological intelligent systems. These biological traits may be exemplified by continual and causal learning for adaptive autonomy; abstraction and generalization for learning from few examples and identifying context-dependent strategies based on prior experience; and the energy- and data-efficiency to extend engineered learning and autonomy across the built environment.
The core hypothesis of BRAID is that the characteristic capabilities of biological learning surpass current machine learning systems in ways that can inspire innovations to meet the urgent needs of mission-critical engineering applications. Successful BRAID projects will transform innovative findings and theories from neuroscience into new frameworks that distill and apply biological learning principles to the design of engineered learning systems. These systems will include new classes of algorithms, circuits, networks, and neuromorphic devices. In formulating a response to BRAID, please consider your potential projects in the context of neuroscience, at any level of computational function of brain-body systems or implementations in neural mechanisms. The inclusion of theoretical neuroscience expertise is mandatory in each proposal.
Any BRAID proposal submitted in response to the EFRI solicitation must address at least two of the following three threads:
- Thread 1: Theoretical Neuroscience
- Thread 2: Brain-informed Hardware Design
- Thread 3: Algorithmic Learning for Resilient Adaptive Technologies
While responses to all three threads are encouraged, note that response to Thread 1 is mandatory. Project summaries must explicitly state which threads the proposed project addresses.
Please read the full solicitation (21-615) at https://www.nsf.gov/pubs/2021/nsf21615/nsf21615.htm.
Webinar recordings are available at https://www.nsf.gov/eng/efma/webinars/efri.jsp.
Webinar slides are available at https://www.nsf.gov/attachments/303513/public/EFRI-FY22-webinar-slides.pdf.
A Letter of Intent is due by September 12, 2022. Preliminary Proposal is due by October 13, 2022. If invited, a Full Proposal will be due by February 7, 2023.
Grace M. Hwang, Ph.D.
Disability and Rehabilitation Engineering (DARE) Program
Chemical, Bioengineering, Environmental, and Transport Systems Division (CBET)
Directorate for Engineering (ENG)
National Science Foundation (NSF)
2415 Eisenhower Ave, Alexandria, VA 22314