[Beowulf] Computational Neuroscience (free online Coursera)

Eugen Leitl eugen at leitl.org
Sat Apr 20 02:27:21 PDT 2013


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)



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