Mathematical methods for neuroscience
Frequency
Only this year
Summary
This course teaches basic mathematical techniques that can be applied on biological and neuroscience challenges. During the course we will focus on solving similarity tasks with stochastic systems, random walks, algebraic topology and statistics.
Content
This course will teach students basic mathematical techniques that can be used to address biological challenges. During the course we will focus on the description of biological systems using statistics, machine learning, algebraic topology, stochastic systems and random walks. The students will learn how to apply these techniques on real-life problems of neuroscience. We will use examples from single cells, such as neurons and astrocytes, and neuronal networks that we will analyze and try to understand better using mathematical tools.
Course content:
* Introduction to mathematical problems
* Statistical methods for biological problems
* Introduction to machine learning
* Application of statistics and machine learning on neuroscience problems
* Introduction to algebraic topology and persistent homology
* Topology of neuronal morphologies
* Random walk and diffusion equations
* Application of random walks on neuronal morphologies
Note
Learning Outcomes:
* Understand mathematical concepts (statistics, random processes, algebraic topology) and their applications in a variety of biological problems
* Set up and solve similarity tasks based on experimental datasets
* Create computational models for the description, analysis and comparison of neuronal morphologies
Keywords
Neuroscience, Statistics, Random walks, Stochastic processes, algebraic topology, neuronal morphologies
Learning Prerequisites
Recommended courses
MATH-493
BIO-369, NX-450, CS-456
In the programs
- Number of places: 20
- Exam form: Project report (session free)
- Subject examined: Mathematical methods for neuroscience
- Lecture: 28 Hour(s)
- Exercises: 14 Hour(s)
- Project: 14 Hour(s)
- Practical work: 14 Hour(s)
- Type: optional