BIO-612 / 3 credits

Teacher: Kanari Lida

Language: English

Remark: Next time: Fall 2024


Only this year


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.


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



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


Neuroscience, Statistics, Random walks, Stochastic processes, algebraic topology, neuronal morphologies

Learning Prerequisites

Recommended courses

BIO-369, NX-450, CS-456


Moodle Link

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

Reference week

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