“We are a diverse group of physicists, mathematicians, computer scientists, bioengineers and neurobiologists, dedicated to this quest. We are motivated by two overarching questions: 1. How do fundamental higher level cognitive phenomena like perception, memory, attention, decision making and motor control emerge from the collective interactions of many dynamical degrees of freedom embedded within networks of neurons and synapses? 2. Can principles of high dimensional statistics and computation provide a unified view of how we as neuroscientists can extract biologically meaningful models of neural systems from large datasets, as well as how neural systems themselves can extract predictive models of their environment from the stream of sensorimotor experience? To pursue these questions, we exploit and extend tools and ideas from a diverse array of disciplines, including statistical mechanics, dynamical systems theory, machine learning, information theory, control theory, and high-dimensional statistics, as well as collaborate with experimental neuroscience laboratories collecting physiological data from a range of model organisms, from flies to humans.”
Realizing a trainable Hopfield associative memory in a driven-dis sipative quantum optical neural network . Keeling, J. Cotler, B.L. Lev, S. Ganguli, In prep
GluD2- and Cbln1-mediated Competitive Synaptogenesis Shapes the Dendritic Arbors of Cerebellar Purkinje Cells Y.H. Takeo, S.A. Shuster, L. Jiang, M. Hu, D.J. Luginbuhl, T. Rlicke, X. Contreras, S. Hippenmeyer, M.J. Wagner, S. Ganguli, L. Luo, Under review PDF