Eran Mukamel

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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

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

Cell Sorting
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.

Cell Sorting
(View in high resolution)