Computational neurosciences: neuronal dynamics

Summary

In this course we study mathematical models of neurons and neuronal networks in the context of biology and establish links to models of cognition. The focus is on brain dynamics approximated by deterministic or stochastic differential equations.

Content

Keywords

neural networks, neuronal dynamics, computational neuroscience, mathematical modeling in biology, applied mathematics, brain, cognition, neurons, memory, learning, plasticity

Learning Prerequisites

Required courses

undergraduate math at the level of electrical engineering or physics majors

undergraduate physics.

Recommended courses

Analysis I-III, linear algebra, probability and statistics

For SSV students: Dynamical Systems Theory for Engineers or "Mathematical and Computational Models in Biology"

Important concepts to start the course

Differential equations, Linear equations,

Learning Outcomes

By the end of the course, the student must be able to:

• Analyze two-dimensional models in the phase plane
• Solve linear one-dimensional differential equations
• Develop a simplified model by separation of time scales
• Analyze connected networks in the mean-field limit
• Predict outcome of dynamics
• Prove stability and convergence
• Describe neuronal phenomena
• Test model concepts in simulations

Transversal skills

• Plan and carry out activities in a way which makes optimal use of available time and other resources.
• Collect data.
• Continue to work through difficulties or initial failure to find optimal solutions.
• Write a scientific or technical report.

Teaching methods

• Video lectures, combined with classroom teaching, classroom discussions, exercises and miniproject. One of the two exercise hours is integrated into the lectures.
• Short mooc-style videos are available as support
• Textbook available as support

Expected student activities

-participate in ALL in-class exercises.

- do all homework exercises (paper-and-pencil)

- study video lectures if you miss a class

- study suggested textbook sections for in-depth understanding of material

- submit miniprojects

Assessment methods

Written exam (70%) & miniproject (30%)

The miniproject is done in teams of 2 students.

Supervision

Resources

Bibliography

Gerstner, Kistler, Naud, Pansinski : Neuronal Dynamics, Cambridge Univ. Press 2014

Ressources en bibliothèque

• Neuronal dynamics: from single neurons to networks and models of cognition / Wulfram Gerstner, Werner M. Kistler, Richard Naud, Liam Paninski

Websites

• https://neuronaldynamics.epfl.ch/
• https://lcnwww.epfl.ch/gerstner/NeuronalDynamics-MOOCall.html

Moodle Link

• https://go.epfl.ch/NX-465

Videos

• https://lcnwww.epfl.ch/gerstner/NeuronalDynamics-MOOCall.html