https://class.coursera.org/compneuro-001/wiki/view?page=syllabus
Syllabus & Schedule About the Course Understanding how the brain works is one of the fundamental challenges in science today. This course will introduce you to basic computational techniques for analyzing, modeling, and understanding the behavior of cells and circuits in the brain. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information. Recommended Background: Familiarity with basic concepts in linear algebra, calculus, and probability theory. Specifically, ability to understand simple equations involving vectors and matrices, differentiate simple functions, and understand what a probability distribution is. For video lectures reviewing these topics, please visit the Linear Algebra, Calculus, and Probability sections of Khanacademy.org. For homeworks, some familiarity with Matlab or Octave would be useful. No prior background in neuroscience is required. Recommended Textbook: The lectures will roughly follow topics covered in the textbook Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems by Peter Dayan and Larry Abbott (MIT Press). Syllabus Topics that we will cover in this course: Basic Neurobiology Neural Encoding and Decoding Techniques Information Theory and Neural Coding Single Neuron Models Synapse and Network Models: Feedforward and Recurrent Networks Synaptic Plasticity and Learning Schedule Week 1: Course Introduction and Basic Neurobiology (Rajesh Rao) Week 2: What do Neurons Encode? Neural Encoding Models (Adrienne Fairhall) Week 3: Extracting Information from Neurons: Neural Decoding and Information Theory (Adrienne Fairhall) Week 4: Simulating Neurons on Computers: Biophysical Models of Neurons (Adrienne Fairhall) Week 5: Simulating Neurons: Simplified Neuron Models (Adrienne Fairhall) Week 6: Modeling Synapses and Networks of Neurons (Rajesh Rao) Week 7: How do Brains Learn? Modeling Synaptic Plasticity and Learning (Rajesh Rao) Week 8: Learning to Act: Reinforcement Learning (Rajesh Rao) _______________________________________________ Beowulf mailing list, [email protected] sponsored by Penguin Computing To change your subscription (digest mode or unsubscribe) visit http://www.beowulf.org/mailman/listinfo/beowulf
