Brief Introduction
The activity of neurons in the brain and the code used by these neurons is described by mathematical neuron models at different levels of detail.
Description
This course gives an introduction to the field of theoretical and computational neuroscience with a focus on models of single neurons. Neurons encode information about stimuli in a sequence of short electrical pulses (spikes). Students will learn how mathematical tools such as differential equations, phase plane analysis, separation of time scales, and stochastic processes can be used to understand the dynamics of neurons and the neural code.
Week 1: A first simple neuron model
Week 2: Hodgkin-Huxley models and biophysical modeling
Week 3: Two-dimensional models and phase plane analysis
Week 4: Two-dimensional models (cont.)/ Dendrites
Week 5: Variability of spike trains and the neural code
Week 6: Noise models, noisy neurons and coding
Week 7: Estimating neuron models for coding and decoding
Before your course starts, try the new edX Demo where you can explore the fun, interactive learning environment and virtual labs. Learn more.
Knowledge
- How mathematical tools such as differential equations, phase plane analysis, separation of time scales, and stochastic processes can be used to understand the dynamics of neurons and the neural code
Computational Neuroscience: Neuronal Dynamics of Cognition
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This course explains the mathematical and computational models that are used in the field of theoretical neuroscience to analyze the collective dynamics of thousands of interacting neurons....
Neuronal Dynamics
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The activity of neurons in the brain and the code used by these neurons is described by mathematical neuron models at different levels of detail....
