Biology & Life Sciences
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.

Course Details

Language English
Duration 7 week
Effort 6 hour/week
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.

What you 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

Prerequisites

Calculus, differential equations, probabilities.

Recommended textbook: "NEURONAL DYNAMICS - from single neurons to networks and cognition", Cambridge Univ. Press 2014

Plan

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

Course instructors

Prof. Wulfram Gerstner

After studies of Physics in Tübingen and at the Ludwig-Maximilians-University Munich (Master 1989), Wulfram Gerstner spent a year as a visiting researcher at UC Berkeley. He received his PhD in Theoretical Physics from the Technical University Munich in 1…

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