Computational Neuroscience

Computational Networking Pre-requisitesComputational Networking Pre-requisitesRecommended 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/Octave or Python would be useful. No prior bac

Syllabus

Topics that we will cover in this course:

Basic Neurobiology
Neural Encoding
Neural Decoding
Information Theory
Modeling Single Neurons
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 (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)

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Computational Neuroscience

Syllabus

Schedule

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