Seminar Computational Intelligence E (708.115)

  SS 2015

Institut für Grundlagen der Informationsverarbeitung (708)
 

Leaders of the Seminar:
Assoc. Prof. Dr. DI Robert Legenstein
O.Univ.-Prof. Dr. Wolfgang Maass

Office hours: by appointment (via e-mail)

E-mail:
maass@igi.tugraz.at
robert.legenstein@igi.tugraz.at

Homepage: www.igi.tugraz.at/maass/

   

Location: IGI-seminar room, Inffeldgasse 16b/I, 8010 Graz
time:   Wednesday 4.15 pm
starting on: March 11th
, 2015

Content of the seminar:

This seminar will introduce into research related areas of Computational Intelligence:

1. The new EU-Flagship Project "Human Brain Project",
http://www.humanbrainproject.eu/
http://www.igi.tugraz.at/maass/nonexperts.html
in particular into the research area "Principles of Brain Computation" in this project, for which our Institute is responsible.

2. New research in machine learning and artificial neural networks (e.g. deep belief nets).

3. Machine learning and robotics research for the EU-project AMARSi http://www.amarsi-project.eu/


This seminar is designed for master students in their second year, or also in the first year, if they take simultaneously NN B (or have already
taken one of the courses listed below).

The papers and talks will be on an introductory level, and will be accessible to an audience with minimal technical background. One didactical goal
of the seminar is to provide experience in giving presentation, which can be practiced here in a relaxed setting.
In addition, students can explore research topics in which they might be interested for a master thesis or -project.

One of the courses NN A, NN B, ML A, ML B suffices as background.





Talks:


Wed, 22.04.2015: Journal Club

    Anja Karl, on Nakahara, H., & Hikosaka, O. (2012). Learning to represent reward structure: A key to adapting to complex environments.
      Neuroscience research, 74(3), 177-183.
      http://www.sciencedirect.com/science/article/pii/S0168010212001800

 
Thu, 30.04.2015, 16:15 pm:

    Wolfgang Maass on recent publications about neural network models with a stable balance of excitation and inhibition,
      in particular on Stepp, N., Plenz, D., & Srinivasa, N. (2015). Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks. PLoS Computational Biology, 11(1).
      (notes and further references  will be sent out later)


 



      



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