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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 senior 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:

1. Basic Neurobiology
2. Neural Encoding
3. Neural Decoding
4. Information Theory
5. Modeling Single Neurons
6. Synapse and Network Models: Feedforward and Recurrent Networks
7. 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 (Adrienne Fairhall)
• Week 4: Information and Coding Principles (Adrienne Fairhall)
• Week 5: Simulating the Brain from the Ground Up: Models of Single Neurons (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)

Recommended reading

• Week 1: Dayan and Abbott, Theoretical Neuroscience, Chapter 1 and Mathematical Appendix
• Week 2: Dayan and Abbott, Theoretical Neuroscience, Chapter 2; Spikes, Rieke et al, appendices
• Week 3: Dayan and Abbott, Theoretical Neuroscience, Chapter 3
• Week 4: Dayan and Abbott, Theoretical Neuroscience, Chapter 4
• Week 5: Dayan and Abbott, Theoretical Neuroscience, Chapters 5 & 6
• Week 6: Dayan and Abbott, Theoretical Neuroscience, Chapter 7
• Week 7: Dayan and Abbott, Theoretical Neuroscience, Chapters 8 & 10
• Week 8: Dayan and Abbott, Theoretical Neuroscience, Chapter 9