BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the Brain (R01 Clinical Trial Not Allowed)
This NIH BRAIN Initiative funding opportunity supports new or substantially advanced theories, mechanistic or predictive models, and computational or statistical methods that improve quantitative understanding of brain function across scales. Tools must address complex neural and behavioral data and be made broadly available to the neuroscience research community.
Lifelong Learning AI via neuro inspired solutions
AI embedded in real systems, such as in satellites, robots and other autonomous devices, must make fast, safe decisions even when the environment changes, or under limitations on the available power; to do so, such systems must be adaptive in real time. To date, edge computing has no real adaptivity – rather the AI must be trained in advance, typically on a large dataset with much computational power needed; once fielded, the AI is frozen: It is unable to use its experience to operate if environment proves outside its training or to improve its expertise; and worse, since datasets cannot cover all possible real-world situations, systems with such frozen intelligent control are likely to fail. Lifelong Learning is the cutting edge of artificial intelligence - encompassing computational methods that allow systems to learn in runtime and incorporate learning for application in new, unanticipated situations. Until recently, this sort of computation has been found exclusively in nature; thus, Lifelong Learning looks to nature, and in particular neuroscience, for its underlying principles and mechanisms and then translates them to this new technology. Our presentation will introduce a number of state-of-the-art approaches to achieve AI adaptive learning, including from the DARPA’s L2M program and subsequent developments. Many environments are affected by temporal changes, such as the time of day, week, season, etc. A way to create adaptive systems which are both small and robust is by making them aware of time and able to comprehend temporal patterns in the environment. We will describe our current research in temporal AI, while also considering power constraints.
Machine learning for measuring and modeling the motor system
If we can make computers play chess, why can't we make them see?
If we can make computers play chess and even Jeopardy and Go, then why can't we make them see like us? How does our brain solve the problem of seeing? I will describe some of our recent insights into understanding object recognition in the brain using behavioral, neuronal and computational methods.
A Changing View of Vision: From Molecules to Behavior in Zebrafish
All sensory perception and every coordinated movement, as well as feelings, memories and motivation, arise from the bustling activity of many millions of interconnected cells in the brain. The ultimate function of this elaborate network is to generate behavior. We use zebrafish as our experimental model, employing a diverse array of molecular, genetic, optical, connectomic, behavioral and computational approaches. The goal of our research is to understand how neuronal circuits integrate sensory inputs and internal state and convert this information into behavioral responses.