Computational Neuroscience

This area of research encompasses a diverse set of approaches in which mathematical or computational tools are used to better understand the nervous system. Computer modeling ranges from simulations of the kinetics of single ion channels, to biologically realistic single-neuron models, network models, and models of cognitive processes.

Experimental techniques include the creation of neural hybrid systems - interfaces between biological neurons and computer-simulated or micro-engineered components; and real-time feedback control allowing computational analysis of an ongoing data stream to be used to dynamically interact with the biological preparation.

Graduate training in Computational Neuroscience is supported by an NIH training grant "From Cells to Systems and Applications: Computational Neuroscience Training at Emory & Georgia Tech". Each year, 3 incoming students are selected as new Fellows and are awarded 2 year stipends through this training grant. Graduate Fellows complete the general Neuroscience program requirements, including core computational neuroscience training activities. These include a regularly scheduled Methods Clinic and Journal Club that are designed to familiarize experimentalists with computational and theoretical approaches, and vice versa. Applicants to the Neuroscience graduate program interested in this training program should contact the Training Program Director, Prof. Dieter Jaeger for further details. Also see our website for more information: Computational Neuroscience Training Grant

Faculty with interests in Computational Neuroscience: