Leenoy investigates how function and computation emerge from the coordinated activity of large neuronal populations. She is particularly interested in the interface between physics and neuroscience, and in developing theoretical approaches that can uncover not only the principles of brain function but also new physics concepts guiding this complex biological system. Recent work of Leenoy’s includes modeling the collective activity of hippocampal neurons, writing down Ising models (maximum entropy) for population activity, and using renormalization group-like approaches to coarse-graining neural dynamics.
Leenoy received her PhD from Princeton University, advised by William Bialek, David Tank and Carlos Brody. Earlier, she completed her Masters studying physics and biology in the Lautman honors program in Tel Aviv University.
She loves large waterfalls, fresh snow, Debussy and dark chocolate.
leenoy at uw dot edu
Fereshteh has a background in Control theory, Machine learning, and Theoretical Neuroscience. During her PhD at Bernstein Center Freirburg, she investigated nonlinear dynamics of interaction and competition between subnetworks of spiking networks. Later, she showed that residual neural networks (a type of ANNs) also follow similar dynamics of competition. She is currently interested in plasticity and dynamics of learning, and in particular how inhibitory neurons shape these dynamics. Fereshteh aims at finding emerging network properties that can explain functions and computation in the brain. To this end, she uses tools such as control theory and nonlinear dynamics.