Mittagsseminar (in cooperation with A. Steger, D. Steurer and B. Sudakov)

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

Duration: 30 minutes

Location: OAT S15/S16/S17

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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