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227-1037-00L 6 Credits BSC , MSC , NDS D-HEST , D-MAVT , D-MATH , D-PHYS , D-INFK , D-ITET
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Introduction to Neuroinformatics

Lecturers & Examiners: Prof. Dr. Giacomo Indiveri, Prof. Dr. Valerio Mante, Prof. Dr. Benjamin Grewe, Prof. Dr. Melika Payvand
VVZ CR 3.43

Last Updated: 2026-02-05 16:30:15

Abstract

The course provides an introduction to the functional properties of neurons. Particularly the description of membrane electrical properties (action potentials, channels), neuronal anatomy, synaptic structures, and neuronal networks. Simple models of computation, learning, and behavior will be explained. Some artificial systems (robot, chip) are presented.

Objective

Understanding computation by neurons and neuronal circuits is one of the great challenges of science. Many different disciplines can contribute their tools and concepts to solving mysteries of neural computation. The goal of this introductory course is to introduce the monocultures of physics, maths, computer science, engineering, biology, psychology, and even philosophy and history, to discover the enchantments and challenges that we all face in taking on this major 21st century problem and how each discipline can contribute to discovering solutions.

Content

This course considers the structure and function of biological neural networks at different levels. The function of neural networks lies fundamentally in their wiring and in the electro-chemical properties of nerve cell membranes. Thus, the biological structure of the nerve cell needs to be understood if biologically-realistic models are to be constructed. These simpler models are used to estimate the electrical current flow through dendritic cables and explore how a more complex geometry of neurons influences this current flow. The active properties of nerves are studied to understand both sensory transduction and the generation and transmission of nerve impulses along axons. The concept of local neuronal circuits arises in the context of the rules governing the formation of nerve connections and topographic projections within the nervous system. Communication between neurons in the network can be thought of as information flow across synapses, which can be modified by experience. We need an understanding of the action of inhibitory and excitatory neurotransmitters and neuromodulators, so that the dynamics and logic of synapses can be interpreted. Finally, simple neural architectures of feedforward and recurrent networks are discussed in the context of co-ordination, control, and integration of sensory and motor information. Connections to computer science and artificial intelligence are discussed, but the main focus of the course is on establishing the biological basis of computations in neurons.

Resources

Learning Materials (Links)

General Information

Language
English
Levels
BSC , MSC , NDS
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
None
Digital
The exam takes place on devices provided by ETH Zurich.
multiple choiceDistance Exams will not be offered. For exchange students there is however the opportunity to request a preponed oral exam.

Course Components

Type Title Time & Place Hours
lecture Introduction to Neuroinformatics
  • Thu 08:15-10:00 (NO C 60)
2 h weekly
exercise Introduction to Neuroinformatics
  • Thu 10:15-11:00 (NO C 60)
1 h weekly
independent project Introduction to Neuroinformatics
Self-study, no fixed presence required.
No time listed 1 h weekly

Offered In