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BE 530 Computational NeuroscienceCourse description: This course will develop theoretical and computational approaches to structural and functional organization in the brain. The course will cover: (i) the basic biophysics of neural responses, (ii) neural coding and decoding with an emphasis on sensory systems, (iii) approaches to the study of networks of neurons, (iv) models of adaptation, learning and memory, (v) models of decision making, and (vi) ideas that address why the brain is organized the way that it is. The course will be appropriate for advanced undergraduates and beginning graduate students. A knowledge of multi-variable calculus, linear algebra and differential equations is required (except by permission of the instructor). Prior exposure to neuroscience and/or Matlab programming will be helpful.
Methods: Linear algebra Probability theory Information theory Differential equations Machine learning Dimensionality reduction
Systems: The role of natural stimuli Coding in early Vision / Audition / Olfaction / Somatosensation Memory Motor control Shape and form vision
Pre-requisites: Mathematics: Knowledge of multi-variable calculus, some linear algebra and some differential equations is necessary for this course. The methods will be developed in class for the benefit of students without much exposure to this material. Students without some prior background must have the permission of the instructor to take this class. Computation: Prior knowledge of Matlab will be useful, but students will go through programming exercises to develop their skills. Neuroscience: Basic knowledge of the architecture of the brain, and of the mechanisms of neural signaling will be very useful. However, for the benefit of students from physics and bioengineering without background in neuroscience, the necessary material will also be developed in class. Faculty
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Department of Bioengineering
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