Eran Mukamel I have moved to UCSD! If you are not redirected automatically, follow the link to UCSD My research explores how complex biological networks interact across spatial and time scales during brain development and in support of brain function during waking and unconscious states. Brain development requires billions of individual neurons and glial cells to form the web of connections that make up the brain's computational circuits. A key component of healthy brain development is the refinement of cells' specialized phentotypes through multiple epigenetic processes. I am interested in how DNA methylation, an epigenetic modification which is long-lasting yet flexible, may serve as a layer of information processing during brain development. General anesthesia is a cornerstone of modern medicine, but how general anesthesia drugs alter brain network dynamics to disrupt the waking state remains largely uncharted. My collaborators and I have taken a new empirical approach to this issue, combining multi-scale electrophysiology in humans with statistical signal processing and computer modeling of neural networks. I also use computational and theoretical techniques to create new tools for empirical studies of the brain, such as methods for super-resolution fluorescence microscopy and multi-cellular calcium imaging (see below for details and software). Moving to UCSD! I'm thrilled to be joining the Cognitive Science faculty at the University of California, San Diego starting in July, 2014! Contact me at emukamel [at] salk [dot] edu.

Recent papers Eran A. Mukamel, Elva Pirondini, Behtash Babadi, et al. A Transition in Brain State During Propofol-Induced Unconsciousness J. Neurosci. 34:839 (2014). Ryan Lister, Eran A. Mukamel, Joseph R. Nery, Mark Urich, Clare A. Puddifoot, Nicholas D. Johnson, Jacinta Lucero, Yun Huang, Andrew J. Dwork, Matthew D. Schultz, Miao Yu, Julian Tonti-Filippini, Holger Heyn, Shijun Hu, Joseph C. Wu, Anjana Rao, Manel Esteller, Chuan He, Fatemeh G. Haghighi, Terrence J. Sejnowski, M. Margarita Behrens, Joseph R. Ecker. Global Epigenomic Reconfiguration During Mammalian Brain Development, Science DOI: 10.1126/science.1237905. (2013). Our paper was featured in NIH Director Francis Collins' blog and selected as one of the "10 Best of 2013" by NIMH Director Tom Insel (#4!). Research Highlight in Genome Biology by Ambre Bender and Michael Weber A Science Perspective on our paper by Harrison Gabel and Michael Greenberg: The Maturing Brain Methylome A brief movie about our work: Some news coverage in The Scientist, The New Scientist, Medical Express, U-T San Diego, Red Orbit, the Cold Spring Harbor DNA Learning Center Blog and ABC (Spanish). Eran A. Mukamel and Mark J. Schnitzer, Unified Resolution Bounds for Conventional and Stochastic Localization Fluorescence Microscopy, Physical Review Letters 109, 168102 (2012). Eran A. Mukamel and Mark J. Schnitzer, Unified Resolution Bounds for Conventional and Stochastic Localization Fluorescence Microscopy, Physical Review Letters 109, 168102 (2012). Eran A. Mukamel, Hazen Babcock and Xiaowei Zhuang, Statistical Deconvolution for Superresolution Fluorescence Microscopy, Biophysical Journal (2012). DeconSTORM software and documentation are available for download. Two modes of phase-amplitude coupling in human cortical electrical dynamics under general anesthesia, presented at Statistical Analysis of Neural Data 6 and at Computational and Systems Neuroscience (COSYNE) 2012 in Salt Lake City, UT. My Ph.D thesis is titled "Biophysical modeling and optical imaging tools for studies of cerebellar motor learning". (PDF; abstract only) All publications

DeconSTORM: Analysis of Super-Resolution Fluorescence Microscopy by Statistical Deconvolution We have recently introduced the DeconSTORM algorithm for analysis of stochastic optical reconstruction micrscopy (STORM), photoactivated localization microscopy (PALM), and related techniques. Our computational analysis approach is described in this publication, and a MATLAB software toolbox is available for implementing our algorithm. Automated Cell Sorting If you are interested in automated analysis of calcium imaging data sets, please see our publication and software below: Eran A. Mukamel, Axel Nimmerjahn, Mark J. Schnitzer, "Automated Analysis of Cellular Signals from Large-Scale Calcium Imaging Data". We have posted some sample code and data that may help you get started using Cellsort. There is a nice overview of the challenge and discussion of our approach by Alex Kwan in the HFSP Journal. The image at left illustrates the results of our cell sorting procedure, which estimates cellular spatial filters (top left), calcium-dependent fluorescence dynamics (bottom left), as well as the underlying spike trains (bottom right). The MATLAB toolbox, CellSort, that accompanies our paper can be downloaded from the MathWorks FileExchange here. You can preview the documentation here. (View in high resolution)