Integrated Calendar

Understanding the dynamics of a complex fluid on a macro scale requires thorough understanding of the dynamics of its constituents on a micro scale. The latter consists of studying the individual dynamics of a single soft micro-object in an external flow and its back-reaction on the flow field, as well as interactions with other micro-objects in the flow.
Using a novel experimental method we were able to conduct long time observation of one or more vesicles subject to an external linear flow field. This allowed us to investigate the role of thermal noise in vesicle dynamics and to confront the experimental results with theoretical/numerical predictions regarding a vesicle dynamical state called trembling. We have shown that the thermal noise is significantly amplified due to coupling with the complex dynamics of the vesicle in trembling state, and causes excitation of higher order odd modes and concavities in the vesicle shape, similar to those, observed in wrinkling type instability. Our main conclusion was that the existing theoretical/numerical models without thermal noise are inappropriate for the description of the trembling state as it is observed in the experiments.

For a mechanistic understanding of brain function, it is important to understand the relation between patterns of activity and connectivity in neural networks. My lab is studying this relation in the retina by classifying its neurons into cell types, and mapping the connections between types. I will describe preliminary results concerning the connections of the J type of ganglion cell, and what they suggest about the mechanism of its direction selectivity. To enable our neuroscience research, we have used machine learning and social computing to build systems that analyze light and electron microscopic images through a combination of artificial and human intelligence. The most exciting recent example is EyeWire, an online community that mobilizes the public to map the retinal connectome by playing a coloring game. I will conclude by describing our beginning efforts to search for the cell assembly, a pattern of connectivity hypothesized by Hebb in 1949 as a structural basis of long-term memory.