Computational Neuroscience Research
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.
Faculty with interests in Computational Neuroscience:
- Gordon Berman
- Alan Emanuel
- Candace Fleischer
- Ming-fai Fong
- Bilal Haider
- Shawn Hochman
- Dieter Jaeger
- Shella Keilholz
- Robert Liu
- Jeffrey Markowitz
- Svjetlana Miocinovic
- Farzaneh Najafi
- Ilya Nemenman
- Chethan Pandarinath
- Marie-Claude Perreault
- Christopher Rodgers
- Annabelle Singer
- Sam Sober
- Lena Ting
- Stephen Traynelis
- Yun Wang