Heiman lab I "Investigating the Roles of Scn4b in Huntington's Disease Pathogenesis"

Topic: Regression coefficient estimation from remote sensing maps.

Ten minutes for your mind@2:50 every day at 2:50 pm in multiple time zones:Europa@2:50, EET, Athens, Helsinki (UTC+2) (7:50 am EST) https://us02web.zoom.us/j/88298032734Atlantica@2:50, EST, New York, Toronto (UTC-4) https://us02web.zoom.us/j/85349851047Pacifica@2:50, PST, Los Angeles, Vancouver (UTC=7) (5:50 pm EST) https://us02web.zoom.us/j/85743543699Almost everything works better again if you unplug it for a bit, including your mind. Stop by and unplug. Get the benefits of mindfulness without the fuss.@2:50 meets at the same time every single day for ten minutes of quiet together.No pre-requisite, no registration needed.Visit the website to view all @2:50 time zones each day.at250.org or at250.mit.edu

Speaker: Charles Doran (University of Alberta)Title: Fibration and Degeneration in Calabi-Yau GeometryAbstract: At String-Math 2015 in Sanya, I gave evidence for a new geometric duality that conjecturally connects mirror pairs of Calabi-Yau manifolds with extra structure: fibrations on one side and degenerations on the other. The “DHT mirror symmetry” conjecture unifies mirror constructions for the Calabi-Yau and Fano/Landau-Ginzburg cases. I will review the status of the DHT conjecture in several settings and describe proven implications in Hodge theory, geometry, and physics.

Talk Title: Neuroscience, behavior, and what's in-betweenAbstract: I'll present an overview of a forthcoming book about how we can link neuroscience to cognition and behavior. Drawing on several little-known results in early computer science, I'll describe how patterns in behavior can rigorously imply the existence of particular unobserved states and structures. This provides a foundation for linking behavioral regularities to what must be present in neural implementations. The resulting states are often re-describable in abstract terms more familiar to cognitive science, like "sets", "numbers", "stacks", etc. I'll highlight the implementation of "stacks", commonly used for grammars, and show how to characterize the space of possible neural implementations, including with subsystems/circuits operating in serial and parallel. The approach provides a set of concrete hypotheses, a guide for neural data analysis, and points towards a method for understanding structure in modern AI systems, including LLMs. I'll conclude by suggesting a Marr-like framework in which the bridges between levels can be made rigorous, connecting behavior, high-level theorizing, and neural implementation.Bio: I completed my PhD from MIT BCS in 2011. I was a postdoc and a faculty member at the University of Rochester until 2018, and then moved to psychology and neuroscience at UC Berkeley. My lab works on language, numerical cognition, and spanning cognition and computation.Webinar Link: https://us02web.zoom.us/j/89002014229?pwd=bzZuZGh6cVhOSjJ6TlNZVHgrRnNaQT09Followed by a reception with food and drink in 3rd floor atrium

Peter Hull (Brown University)