Computational Neuroscience and Neural Engineering

The goal of scientists who are engaged in computational neuroscience research is to build mathematical and/or computer-based models that help to explain existing biological data, but more importantly, that provide a theoretical framework that encapsulates our emerging understanding of the sensory, motor, and cognitive functions of the brain. Modeling efforts span many levels of neuroscience research, ranging from biophysically detailed models of ion channels, synaptic transmission, and dendritic integration, up through systems-level models of visual perception, sensory-motor control, memory, and language, as well as neurological disorders such as Parkinson’s disease.


Neural engineering research by our training faculty focuses on brain-machine interfaces, and on applications of brain-like processing strategies to help solve difficult technical problems. Examples include neurally-inspired approaches to sensory adaptation, visual face and object recognition, speech recognition, motor output, enhancing memory and learning, and the control of complex humanoid robots. As examples of our accomplishments, neural engineers in the NGP have developed communication interfaces between electronic circuits and neural tissue, with applications to neural prosthetics and brain implants, designed hybrid optical-electronic hardware systems capable of implementing extremely fast, low power “neuromorphic” computations, and lead major efforts in Neuroinformatics, such as pioneering the construction of databases, and visualization and simulation tools for neuroscience research.

Faculty Members

Theodore W. Berger
James M Finley
Laurent Itti
Jason J Kutch
Gerald E. Loeb
Maja J Mataric
Bartlett W. Mel
Toben H. Mintz
Nicolas Schweighofer
Armand R. Tanguay, Jr.
Francisco Valero-Cuevas