John Patrick Cunningham, Phd - US grants

Flexibility of thought and action is a hallmark of human and animal behavior. While the basic mechanism of long-timescale flexibility (synaptic modifications) is largely agreed upon, the neural signatures and mechanistic underpinnings of short-timescale flexibility are not yet understood. Here we investigate the motor cortex (i.e., the primary motor cortex and adjacent premotor areas), which participates in the production of a great variety of behaviors, each requiring different internal computations. Yet it remains unknown how the motor cortex accomplishes this flexibility. Indeed, we lack two very basic pieces of knowledge. First, what physical mechanisms could allow such flexibility? Second, what are the empirical signatures of such mechanisms; how could they be recognized in neural response data? Unfortunately the currently available conceptual and analytical tools are ill-suited to study flexibility- e.g., a typical approach is to characterize a fixed relationship between a neuron's response patterns and a measured stimulus or movement parameter. Viewed using standard tools, neurons in many cognitive and motor areas appear to display complex responses that have little if any reliable relationship with identified quantities. This raises a difficult question: how should one attempt to characterize a system whose central feature is flexibility? A key first step is to be able to identify neural signatures of changing computations. Fortunately, modern theoretical neuroscience offers a key idea: large recurrent networks are reservoirs of response components from which complex computations can be built. Different computations require different components, and components are a unique weighted combination of neurons - a single 'neural dimension' within the n -dimensional space of n neurons. Thus, different computations occur in 'orthogonal subspaces' - disjoint sets of neural dimensions. Does this computational structure exist in biological neural networks? Answering this question requires experiments and analytical tools that can identify this potential signature of flexibility within a population of heterogeneous neural responses. RELEVANCE (See instructions): This project will advance understanding of the ability of the motor cortical system to flexibly and quickly change the computation it performs. This research will have biomedical impact that directly speaks to NINOS goals: improved understanding of the neural basis of movement should lead to better prosthetic technologies, assistive technologies, and treatments for the millions who suffer from motor disorders.

The International Conference on Machine Learning (ICML) is considered to be the premier conference in Machine Learning, both from an educational and scientific standpoint. ICML encompasses topics on all facets of Machine Learning, and solicits papers on problem areas, research topics, learning paradigms, and approaches to evaluation. It is a key forum for exchange of ideas in the Machine Learning community, and features scientific poster sessions, educational tutorials, forward-looking workshops, and invited talks. This project offers awards to students to help defray their travel costs, promoting a broader societal reach for the conference. Promoting education and participation in cutting edge data science is central to the mission of the NSF, and this project and its awards speak directly to that mission.

ICML 2016 will be held in New York City. We have estimated itemized costs based on previous ICML conferences. Students funded via this project will be selected by a peer review process based on their financial needs, alignment of research areas, and participation in the conference. These awards will offer the opportunity to network with experts in the area and gain valuable insights into the cutting edge of Machine Learning research. This will positively impact the depth and breadth of their research as well as quality of their dissertations. Long-term career benefits should also result, as ICML has a number of industrial sponsors who set up booths and discuss Machine Learning-oriented job opportunities within their own companies. The ICML 2016 scholarship program follows the successful scholarship programs from the last several years. The requested funds will be used exclusively to help defray the travel and registration costs of students attending the conference. These student scholarships are very important for encouraging student participation in this premier conference and for shaping the future of the field as a whole.

Understanding how a healthy brain interprets sensory signals and guides actions, and why an unhealthy brain fails to perform these functions properly, is a profound and ambitious goal of 21st century science. Integrating knowledge of neural circuit function into a coherent picture of perception, cognition and action requires extraordinary cooperation and coordination between three research areas: experimentation, data analysis and modeling. The National Science Foundation Theory Team at Columbia University will unite exceptional resources in statistical data analysis and theoretical modeling with an extensive network of experimental collaborators to address the enormous challenges facing neuroscience. Never has the need been greater for theoretical insights and sophisticated data analysis. The field of neuroscience is facing a torrent of complex data from a system that is, itself, extraordinarily complex. Future progress requires developing the ability to extract knowledge and understanding from these data through analyses and modeling that capture the essence of what they mean. The goal of the NeuroNex Theory Team at Columbia is to establish, through the quality of its research, the excellence of its trainees, and the impact of its visitor, dissemination, and outreach programs, a new cooperative paradigm that will move neuroscience to unprecedented levels of discovery and understanding.

High-density electrode recording, wide-field calcium imaging and complex connectivity mapping are bringing neuroscience into an era of extensive multi-area and even whole-brain studies of neural activity and circuitry. The neuroscience community desperately needs new ways of interpreting data obtained from different species using myriad techniques and for thinking about neural processing over large length and time scales and across multiple brain areas. In response to these challenges, two major goals will drive and define research at the NeuroNex Theory Team at Columbia: first, integrating the analysis methods and theoretical models used to infer meaning from data with each other and with the experiments that generate these data; and second, providing analytic tools and theoretical frameworks to understand interactions between multiple brain regions and to draw important overarching lessons from experiments exploiting a variety of techniques across different species. Progress will be made through a tight integration of theoretical techniques with outstanding experimental collaborators working on a variety of systems and species. Graduate and postdoctoral training will stress technical excellence and broad perspectives in both theoretical and experimental neuroscience. Outreach will be made to other researchers through visitor and exchange programs, sponsored meetings and dissemination of research results and high-quality, user-friendly software. Outreach will be made to the broader community by sharing the excitement of neuroscience research with elementary and high school students and with the general public. This NeuroNex Theory Team award is co-funded by the Division of Emerging Frontiers within the Directorate for Biological Sciences, the Division of Physics and the Division of Mathematics within the Directorate of Mathematical and Physical Sciences, and by the Division of Brain and Cognitive Sciences within the Directorate of Social, Behavioral and Economic Sciences, as part of the BRAIN Initiative and NSF's Understanding the Brain activities.