Orit Shefi is an Associate Professor in the Faculty of Engineering at Bar-Ilan University. She is the head of the Neuro-engineering and Regeneration laboratory and a member of the Bar Ilan Institute of Nanotechnologies and Advanced Materials. In 2005, she earned her Ph.D. from the Department of Physics at Tel-Aviv University. From 2005-2008, she was a postdoctoral fellow at the University of California, San Diego. Her research focuses on nanotechnologies for neuronal engineering, 3D platforms for directed neuronal regeneration, magnetic manipulations of cells, and drug delivery tools.
Computational modeling of signal propagation in neurons is critical to our understanding of basic principles underlying brain organization and activity. Exploring these models is used to address basic neuroscience questions as well as to gain insights for clinical applications. The seminal Hodgkin Huxley model is a common theoretical framework to study brain activity. It was mainly used to investigate the electrochemical and physical properties of neurons. The influence of neuronal structure on activity patterns was explored, however, the rich dynamics observed in neurons with different morphologies is not yet fully understood. Here, we study signal propagation in fundamental building blocks of neuronal branching trees, unbranched and branched axons. We show how these simple axonal elements can code information on spike trains, and how asymmetric responses can emerge in axonal branching points. This asymmetric phenomenon has been observed experimentally but until now lacked theoretical characterization. Together, our results suggest that axonal morphological parameters are instrumental in activity modulation and information coding. The insights gained from this work lay the ground for better understanding the interplay between function and form in real-world complex systems. It may also supply theoretical basis for the development of novel therapeutic approaches to damaged nervous systems.
Interested in our research?
We welcome exceptional students and postdocs with a background in Engineering, Biology, Chemistry, Physics and/or Material Sciences.
Please email email@example.com