December 1, 2014
Davis Auditorium, CEPSR
Speaker: Dr. Dmitri "Mitya" B. Chklovskii, Group Leader for Neuroscience, Simons Center for Data Analysis, Simons Foundation
Despite our extensive knowledge of the biophysical properties of neurons, there is no commonly accepted algorithmic theory of neuronal function. Here we explore the hypothesis that a neuron performs online matrix factorization of the streamed data. By starting with a matrix factorization cost function we derive an online algorithm, which can be implemented by neurons and synapses with local learning rules. We demonstrate that such network performs feature discovery and soft clustering. The derived algorithm replicates many known aspects of sensory anatomy and biophysical properties of neurons. Thus, we make a step towards an algorithmic theory of neuronal function, which should facilitate large-scale neural circuit simulations and biologically inspired artificial intelligence.
Before coming to the Simons Foundation in 2014, Mitya Chklovskii was a group leader at the Howard Hughes Medical Institute’s (HHMI) Janelia Farm Research Campus in Ashburn, Virginia. Chklovskii also initiated and led a collaborative project at HHMI that assembled the largest-ever connectome, a comprehensive map of neural connections in the brain. Before that, he worked at Cold Spring Harbor Laboratory in New York, where he founded the first theoretical neuroscience group, having worked there as a first assistant, and later an associate professor. As group leader for neuroscience, Chklovskii will lead an effort to understand how the brain analyzes complex datasets streamed by sensory organs, in an attempt to create artificial neural systems. He holds a Ph.D. in physics from the Massachusetts Institute of Technology.
Hosted by Aurel A. Lazar.