Starts at: February 6, 2014 4:30 PM
Ends at: 5:30 PM
Location: Pittsburgh campus: Scaife Auditorium (Room 125), Silicon Valley campus: Room 118, Building 23
Speaker: Surya Ganguli
Affiliation: Stanford University
Refreshments provided: Yes
Pittsburgh campus: Scaife Auditorium (Room 125), Refreshments at 4pm.
Silicon Valley campus: Room 118, Building 23 at 1:30pm PST
Faculty and Students,
If you would like to meet with Surya Ganguli during his visit to CMU on Thursday, February 6th please email Marilyn Patete (email@example.com).
A theory of neural dimensionality, dynamics, and measurement.
In a wide variety of experimental paradigms, neuroscientists tightly control behavior, record many trials, and obtain trial averaged neuronal firing rate data from hundreds of neurons, in circuits containing millions to billions of behaviorally relevant neurons. Such datasets reveal two striking properties: 1) they can be described using a small number of dimensions in firing rate space, and 2) the projections of neural activity onto these dimensions yield a remarkably insightful dynamical portrait of circuit computation. Thus many neuronal datasets are surprisingly simple, and we seem to be able to extract reasonable collective neuronal dynamics despite overwhelming levels of neuronal subsampling. This ubiquitous simplicity raises several profound and timely conceptual questions. What is the origin of this simplicity? What does it tell us about the complexity of brain dynamics? Would neuronal datasets become more complex if we recorded more neurons? How and when can we trust dynamical portraits obtained from only hundreds of neurons in a circuit containing billions of neurons? More generally, what, if anything, can we learn about a complex dynamical system by measuring an infinitesimal fraction of its degrees of freedom? We present a theory of neural dimensionality, dynamics and measurement that answers all of these questions, and we further test this theory in neural recordings from monkeys performing reaching movements.
Surya began his academic career as an undergraduate at MIT, triple majoring in mathematics, physics, and EECS, and then moved to Berkeley to complete a PhD in string theory. There he worked on theories of how the geometry of space and time might holographically emerge from the statistical mechanics of large non-gravitational systems. After this, he chose to pursue the field of theoretical neuroscience, where theories could be tested against experiments. After completing a postdoc at UCSF, he has recently started a theoretical neuroscience laboratory at Stanford. He and his lab now study how networks of neurons and synapses cooperate to mediate important brain functions, like sensory perception, motor control, and memory. He has been awarded a Swartz-Fellowship in computational neuroscience, a Burroughs-Wellcome Career Award at the Scientific Interface, a Terman Award, and an Alfred P. Sloan foundation fellowship.