Dynamical Neuroscience

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Dynamical Neuroscience

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The UCSB Interdepartmental Graduate Degree Program in Dynamical Neuroscience (DYNS) provides strong teaching and state?of?the?art research in computational methods that have the goal of understanding brain-mind interactions across all levels of architecture, from single neurons to complex networks. The program recognizes that mathematical and computational methods are revolutionizing neuroscience. The complexity and volume of neurophysiological data, from single synapses to whole brains, begs for formal approaches that can rigorously test theories, uncover subtle relationships, and extract meaning in the presence of high levels of noise. Within this broad field, different disciplines have emerged. Building on the Nobel Prize winning efforts of Hodgkin and Huxley, computational neuroscience builds biophysically detailed models of single neurons or small networks of neurons. A much newer approach, called computational cognitive neuroscience uses simplified Hodgkin and Huxley models to model large?scale neural networks and to formally test cognitive neuroscience theories of behavior. Network and complexity analyses use results from graph theory, complexity theory, and nonlinear dynamics to uncover fundamental principles of brain organization and function. Finally, signal processing and machine learning approaches use sophisticated algorithms from engineering and computer science to extract signal from noisy neuroscience data. The UCSB DYNS program provides students with a formal approach that unifies these different subfields. Areas of specialization include complex neural networks and computational vision.

The program brings together faculty located in the following seven departments on campus: 1) Chemical Engineering, 2) Computer Science, 3) Electrical and Computer Engineering, 4) Mechanical Engineering, 5) Molecular, Cellular and Developmental Biology, 6) Physics, and 7) Psychological & Brain Sciences.