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The Institute for Neural Computation (INC) is an organized research unit of the University of California at San Diego with 44 members representing 14 research disciplines, devoted to the research and development of a new generation of massively parallel computers through a coherent and cohesive plan of research spanning the areas of neuroscience, visual science, cognitive science, artificial intelligence, mathematics, economics and social science, and computer engineering. View the affiliated faculty list here ...
The INC supports training programs for graduate students and postdoctoral fellows in Cognitive Neuroscience (NIMH). The Center converges neurobiological, cognitive science, computational and engineering resources for understanding the relationship between the mind and the brain. The Center develops new models of brain dynamics, new signal processing techniques based on these models, and the experimental methods to test them. One goal is to study brain activity that supports social interactions.
The Computational Neuroscience specialization is a new facet of the broader Neuroscience graduate program at UCSD . The goal of the specialization is to train the next generation of neuroscientists with the broad range of computational and analytical skills that are essential to understand the organization and function of complex neural systems.
(Nov. 2013) 6th International IEEE EMBS Conference on Neural Engineering
Registration now open.
(11/21/2013) INC Chalk Talk Series
Towards Clinically Viable Neural Prosthetic Systems
(11/04/2013) Joint INC-IEM Neuroengineering Seminar Series
A Miniaturized Brain-Machine-Brain Interface for Restoration of
Function after Brain Injury
(11/04/2013) Joint INC, Calit2 (Qi) CSE Colloquium
Vision Analytics: Confluence of Algorithms, Architectures and Technology
Computational Neuroscience Seminar
Engineering a Large Scale Vision System by Leveraging Semantic Knowledge
(10/18/2013) INC Seminar Series
WirelessHealth: Challenges and Opportunities
(10/14/2013) Qualcomm-BNC-INC Computational Neuroscience Lecture Series
Multivariate Data Classification on Neuromorphic Hardware