Department of Computer Science | Institute of Theoretical Computer Science | CADMO

Theory of Combinatorial Algorithms

Prof. Emo Welzl and Prof. Bernd Gärtner

Mittagsseminar (in cooperation with M. Ghaffari, A. Steger and B. Sudakov)

Mittagsseminar Talk Information

Date and Time: Thursday, May 10, 2012, 12:15 pm

Duration: 30 minutes

Location: CAB G51

Speaker: Stephan Kollmann

Topology Learning in Spiking Recurrent Competitive Networks (Master thesis)

Recently it has been shown that recurrent neural networks with initially random connections and weights can learn the topology of an external input using a rate based neuron model and a Hebbian learning rule. However it was not clear whether these results can be reproduced in a more biologically plausible setting. We show that similar results can also be achieved using a spiking neuron model and a STDP (Spike Timing Dependent Plasticity) learning rule that is based on triplets of spikes (it has been shown that experimental data can be explained well using such a rule). We further analyze the trained network's ability to exhibit certain soft-winner-take-all behavior, namely signal restoration, winner selection and cue integration.


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