Dynamical Systems in Neuroscience

Limited number of participants! Please enroll by sending an email to amir.azizi@rub.de.

Enrollment period: June 1 to July 7, 2017.

Much of our understanding of the neurocomputational properties of brain cells comes from the pioneering studies of Hodgkin and Huxley in the late 40s. They build a detailed model of the membrane potential dynamics of neurons based on the conductivity of various ion channels. Later work on dynamical systems showed that different responses of cells with similar electrophysiology to input currents is due to different bifurcation mechanisms of excitability.

In this course we study the Hodgkin-Huxley (HH) model of neurons and introduce the analytical treatment of non-linear dynamical systems. We will then drive and study a typical reduced HH model analytically and determine different regimes of activity in such a system.

Lecturers

Details

Course type
Lectures
Credits
6
Term
Summer Term 2017

Dates

Lecture
Takes place every week on Wednesday from 10:00 to 12:00 in room NB 3/72.
First appointment is on 19.04.2017
Last appointment is on 26.07.2017
Exercise
Takes place every week on Wednesday from 14:00 to 16:00 in room NB 3/72.
First appointment is on 26.04.2017
Last appointment is on 26.07.2017

Requirements

This course is for students in Physics, Mathematics, Applied Computer Science or Applied Informatics Sciences.

Literature:

Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. 2007 BY: Eugene M. Izhikevich, The MIT Press

Nonlinear dynamics and Chaos. 2001 BY: Steven Strogatz, Westview Press

Analysis of neural excitability and oscillations. 1989 BY: Rinzel John and Ermentrout G. Bard, In Koch C., Segev I. (eds) Methods in Neuronal Modeling, Cambridge, Mass, The MIT Press

About the INI

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Contact

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210

sekretariat@ini.rub.de
www.ini.rub.de