In this thesis inhibition as a means for competition among neurons in an unsupervised learning system is studied. In the first part of the thesis, the role of inhibition in robustness against loss of information in the form of occlusion in visual data is investigated. In the second part, inhibition as a reason for loss of information in the mathematical models of neural system is addressed. In that part, a learning rule for modeling inhibition with lowered loss of information and also a dis-inhibitory system which induces a winner-take-all mechanism are introduced. The models used in this work are unsupervised feature extractors made of biologically plausible neural networks which simulate the V1 layer of the visual cortex.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:20929 |
| Date | 27 March 2018 |
| Creators | Kermani Kolankeh, Arash |
| Contributors | Hamker, Fred H., Hamker, Fred H., Spitsyn, Vladimir Grigorievich, Technische Universität Chemnitz |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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