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227-1034-00L 6 Credits BSC , DR , MSC , NDS D-BSSE , D-MAVT , D-MATH , D-PHYS , D-INFK , D-BIOL , D-ITET , D-HEST
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Computational Vision (University of Zurich)

Lecturers & Examiners: Dr. Daniel Kiper
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student. UZH Module Code: INI402 Mind the enrolment deadlines at UZH:
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Last Updated: 2026-06-01 11:33:01

Abstract

This course focuses on neural computations that underlie visual perception. We study how visual signals are processed in the retina, LGN and visual cortex. We study the morpholgy and functional architecture of cortical circuits responsible for pattern, motion, color, and three-dimensional vision.

Objective

This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.

Content

This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.

Resources

Literature

Books: (recommended references, not required) 1. An Introduction to Natural Computation, D. Ballard (Bradford Books, MIT Press) 1997. 2. The Handbook of Brain Theorie and Neural Networks, M. Arbib (editor), (MIT Press) 1995.

General Information

Language
English
Levels
BSC , DR , MSC , NDS
Frequency
Yearly recurring

Examination

Type
graded semester performance
Registration modalities, date and venue of this performance assessment are specified solely by UZH.

Course Components

Type Title Time & Place Hours
lecture Computational Vision (University of Zurich)
**Course at University of Zurich**
  • Thu 17:15-19:00
  • Thu None-None
2 h weekly
exercise Computational Vision (University of Zurich)
**Course at University of Zurich** Exercise dates by arrangement.
No time listed 1 h weekly
independent project Computational Vision (University of Zurich)
Self-study course - no fixed presence required.
No time listed 1 h weekly

Offered In