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. The specialization is intended for students with backgrounds in neuroscience, physics, chemistry, biology, psychology, computer science, engineering, and mathematics.

The specialization allows Neuroscience students to concentrate on a focused program of rigorous course work in both the theoretical and experimental aspects of computational neuroscience. Students are encouraged to pursue thesis research that includes both an experimental and a computational component, often arranged by the student as a collaboration between two research groups. Upon achievement of degree requirements, students will receive a diploma indicating both their successful completion of the broader Neuroscience Program as well as their specialization in Computational Neuroscience.

The program is focused on these major themes relevant for computational neuroscience research:

• Neurobiology of Neural Systems - the anatomy, physiology, and behavior of systems of neurons, with emphasis on basic phenomenology.
• Advanced Measurement Tools in Neuroscience - Advanced imaging and recording techniques reflecting the impact of experimental physics on neuroscience.
• Algorithms for the Analysis of Neural Data - New algorithms and techniques for analyzing data obtained from physiological recording
• Theoretical Basis for Collective Neural Dynamics - A synthesis of approaches from mathematics and physical sciences as well as biology will be used to explore the collective properties and nonlinear dynamics of neuronal systems.

For information on the academic requirements of the Computational Neuroscience specialization, see the website for the Neuroscience graduate program at UCSD .