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Shaul Druckmann

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I started my own lab at Janelia Research Campus in 2013. My lab focuses on understanding how neurons represent and process information, especially in the context of two salient aspects of neural circuit architecture: high dimensional representations and recurrent connections. I did my postdoctoral work in the lab of Mitya Chklovskii, also at Janelia. The focus of my postdoc research was on the relation between structure and dynamics in neural circuits. I did my PhD in Computational Neuroscience in the Interdisciplinary Center for Neural Computation (ICNC) in the Hebrew University in Jerusalem, with Idan Segev, working on classifying, modeling, and understanding the fascinating diversity of inhibitory interneurons in the neocortex. My undergraduate studies were also taken at the Hebrew University where I studied in the "Amirim" honor program in Humanities alongside my education in Physics. After completing this intensive undergraduate degree program I took a year off to study art in Florence, Italy.

Beyond science, I am interested in Beauty - especially drawing, poetry, opera, and classical music. I enjoy sports, socializing with friends, reading, and hiking.

My research in the Chklovskii lab is focused on neuronal representation and dynamics in overcomplete recurrent neural networks. Such networks occur when a large number of neurons represent the information coming through a much smaller number of input channels (axons). We strive to understand the operation of such networks. These two features, a high ratio of over-completeness and numerous lateral connections, are perhaps the two most defining features of cortex, yet are also those that are most unintuitive for us (or maybe just me) to interpret. As humans we can naturally imagine and understand sequential processes that occur in separate channels but have a much harder time contemplating recurrent processes that are distributed among many interacting elements. Yet as fate would have it, this seems to be one of the organizing principles of the brain. Thus, we must twist the workings of our minds in new ways in order to understand our brains. My other main interest is interacting with data (and those who collect it). There is a magical feeling of finding hidden treasure when you are able to transform your discussions with collaborators and exploratory ideas about how a dataset is behaving into a clear set of hypotheses and the analysis that confronts them.