This project is focused on data-driven development of mechanistic models of laminar neuronal cell populations aiming to establish connections between properties at the level of single neurons to the behavior of large neuronal populations.

To this end, the project will reconstruct excitatory and inhibitory neuronal activity in the primary somatosensory cortex in response to sensory stimulation through combination of experimental data of different modalities (multi-electrode electrophysiological data, optical imaging data using voltage- and calcium-sensitive fluorescent indicators) in a united theoretical framework. The project will run in parallel and interactively with the ongoing model-driven data acquisition effort. Specific developments will include explicit models of signal generation and propagation within single cortical columns and between neighboring columns, for relating experimental observables to the underlying physiological parameters for single neurons and neuronal populations.
*Position description*
Under the general supervision of the PI, the postdoctoral fellow will participate in development of a data-driven computational model that would bridge the gap between single-neuron and population rate levels of description, and make explicit predictions about experimental “observables” associated with spiking and synaptic activity of different cell populations. Responsibilities will also include assisting in data collection and any further development of the existing real-time data acquisition software and hardware that would be necessary for achieving the goals of the model-driven data acquisition effort.
Software development
A. Develop new software and maintain existing software for model fitting of the experimental data of different modalities: optical and electrophysiological.
B. Mathematical model development.
Writing papers
A. Write papers summarizing the scientific results
B. Write abstracts and present at scientific meetings
Data analysis
A. View, process (extract features) and summarize experimental data of different modalities for the comparison with the model output.
B. Generate plots, charts and statistical reports for presentation at scientific meetings and publications
Data archiving
A. Perform back up for relevant modeling and experimental data
B. Manage disk space and document data location.
Assistance during data collection
A. Assist in acquisition of electrophysiological and optical imaging data during experiments designed to validate model predictions.
B. Optimize the software and hardware for real-time data acquisition to incorporate specific requirements for model-driven experiments such as specific stimulation paradigms.
*Knowledge, skills, and abilities*
PhD degree or equivalent in Computational Science, Engineering, Physics or Mathematics or related field is required
Background in scientific programming is required
Demonstrated experience with programming in Matlab, C, and/or Python is required
Demonstrated ability to work independently with high scientific productivity is required
Strong analytical and problem solving skills and attention to details is required
Background in model fitting and optimization techniques is required
Background in signal processing and image analysis is desired
Background in computational neuroscience is desired
Working knowledge of Windows and Linux operating systems and a basic understanding of TCP/IP and network issues is desired
Experience in working in multidisciplinary environment that includes experts in neuronal modeling, computational neuroscience, neurobiology, neuroimaging, radiology and physics is desired
Understanding of brain physiology/biophysics is preferred
Interested applicants please email Dr Anna Devor at adevor(at)ucsd(dot)edu.

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