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April 18, 2018


Wednesday, April 18, 2018

Biostatistics Help: Faculty, Staff, Pre-MD and Post-Doc Walk-In Clinic
11:00 AM - 12:00 PM

Biostatistics consulting is available to all Johns Hopkins University faculty, staff, pre-MDs and post-docs conducting clinical and translational research. 11:00 AM – 12:00 PM Wolfe Street Building Room: E3531 Contact Information Name: Nita James Phone: 410-614-2661 Email:
Extra-genital infections – Seek and Ye Shall Find
12:00 PM - 12:45 PM

Extra-genital infections – Seek and Ye Shall Find Susan Anne Tuddenham, MD, MPH, Assistant Professor of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine
13th Annual Harper Lecture - Population, Family and Reproductive Health - Wednesday Seminar Series
12:15 PM - 1:20 PM

Bloomberg School of Public Health


Population and Family Planning in sub-Saharan Africa

John Bongaarts, PhD
Vice President and Distinguished Scholar
The Population Council

Contact Rachel Reid.


Public Health in Detroit: The Health Commissioner's Perspective
12:15 PM - 1:20 PM

Bloomberg School of Public Health

Office of Public Health Practice and Training
Anna Baetjer Society for Public Health Practice
General Preventative Medicine Residency

Joneigh S. Khaldun, MD, MPH, FACEP
Director and Health Officer
Detroit Health Department

For more information, please contact
Pierre-Emmanuel Jabin "Complexity of some models of interacting biological neurons."
3:00 PM - 4:00 PM


Speaker: Pierre-Emmanuel Jabin, University of Maryland, College Park Abstract: We study some models of for the dynamics of large groups of biological neurons: Those typically consist of large coupled systems of ODE's or SDE's, usually implementing some simple form of integrate and fire. The main question that we wish to address concerns the behavior of such networks as the number of neurons increases. Many particle systems (as they are used in physics, or multi-agent systems in general) naturally come to mind, as it is well known in such cases that propagation of chaos (i.e. the almost independence of each agent) can lead to a reduction in complexity through the direct calculation of various macroscopic densities. However the system under consideration here can be seen as multi-agent system with positive reinforcement so that correlations between neurons never vanish. In an ongoing work with D. Poyato, we first study the case where neurons are essentially fully connected. We show that in spite of this simple topology, the networks may exhibit different measures of complexity which can be characterized through the type of initial connections between neurons.

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