Computational Neuroscience: Vision and Memory

NUMMER: 310504
KÜRZEL: CompNeVM
MODULBEAUFTRAGTE:R: Prof. Dr. Laurenz Wiskott
DOZENT:IN: Prof. Dr. Laurenz Wiskott
FAKULTÄT: Fakultät für Informatik
SPRACHE: Englisch
SWS: 4 SWS
CREDITS: 5 CP
WORKLOAD: 150 h
ANGEBOTEN IM: jedes Sommersemester

BESTANDTEILE UND VERANSTALTUNGSART

a) Vorlesung Computational Neuroscience:
Vision and Memory (310504)

PRÜFUNGEN

FORM: digital schriftlich
ANMELDUNG:
DATUM: 0000-00-00
BEGINN: 00:00:00
DAUER:
RAUM:

LERNFORM

This course is given with the flipped/inverted classroom concept. First, the students work
through online material by themselves. In the lecture time slot we then discuss the material,
find connections to other topics, ask questions and try to answer them. In the tutorial time
slot the newly acquired knowledge is applied to analytical exercises and thereby deepened. I
encourage all students to work in teams during self-study time as well as in the tutorial.

LERNZIELE

After the successful completion of this course the students:
∙ know basic neurobiological facts about the visual system and the hippocampus,
∙ know a number of related models and methods in computational neuroscience,
∙ understand the mathematics of these methods,
∙ can communicate about all this in English.

INHALT

This lecture covers basic neurobiology and models of selforganization in neural systems, in
particular addressing

Learning and self-organization
∙ Hebbian Learning
∙ Neural learning dynamics and constrained optimization
∙ Dynamic field theory

Vision
∙ Receptive fields
∙ Neural maps
∙ Hippocampus
∙ Navigation
∙ Episodic memory
∙ Hopfield Network

VORAUSSETZUNGEN

Keine

VORAUSSETZUNGEN CREDITS

Bestandene Modulabschlussprüfung

EMPFOHLENE VORKENNTNISSE

The mathematical level of the course is mixed but generally
high. The tutorial is almost entirely mathematical. Mathematics required include calculus
(functions, derivatives, integrals, differential equations, ...), linear algebra (vectors, matrices,
inner product, orthogonal vectors, basis systems, ...), and a bit of probability theory (probabilities,
probability densities, Bayes’ theorem, ...).

LITERATUR

AKTUELLE INFORMATIONEN

SONSTIGE INFORMATIONEN