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Neurodynamics (Lecture + Tutorial)




Prerequisites: Lineare Algebra, Analysis I

In this lecture, we will discuss cutting edge science that uses dynamical system, such as networks of neurons to implement computational principles and cognitive functions. We will start with basic concepts of modelling the activity of single neurons. This includes bio-physical models as well as generative models of spiking activity. We learn essential concepts such as attractors, bifurcations, Poincare maps etc to describe and understand the behaviour of dynamical systems. In the second part we will move to complex dynamical systems such as spiking neuronal networks. We will use these networks to compute information using the concept of Liquid State Machines.To link the knowledge acquired in this course with scientific question every second lecture a 30 min 3W session is offered. The three big W are: why should I learn this / what for can I use it / how can it be important my bachelor and master thesis. The lecture will be supplemented by a seminar (Neurodynamics) on dynamical systems in neuroscience. This course is intended for Bachelor students in their third term and for master students as a compulsory module.

Weitere Angaben

Ort: 66/E34
Zeiten: Do. 16:00 - 18:00 (wöchentlich), Fr. 12:00 - 14:00 (wöchentlich)
Erster Termin: Donnerstag, 07.04.2022 16:00 - 18:00, Ort: 66/E34
Veranstaltungsart: Vorlesung und Seminar (Offizielle Lehrveranstaltungen)


  • Cognitive Science > Bachelor-Programm
  • Cognitive Science > Master-Programm
  • Data Science