Computation with spiking neural networks

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dc.contributor.advisorMemmesheimer, R.
dc.contributor.authorUhlmann, Marvin
dc.date.issued2014-10-30
dc.description.abstractUnlike generic spiking neural networks, networks with stable irregular dynamics are stable against small perturbations. Furthermore, big networks were shown have a state space fractured in flux tubes. We describe the novel effect of flux tube merging in big networks and expanded the concept of flux tubes also to very small networks. Using intuition from simple examples, we present an approach for an analytica! description of a state space with flux tubes. This allows to formally understand flux tubes in both small and big networks. Then, we identify a number of computationally beneficia! properties of such state space structures and propose setups that exploit these to perform real spike-time-dependent computation. With the analytica! understanding it may become possible to design network connectivities for specific flux tube dynamics.en_US
dc.embargo.lift2039-10-30
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/5194
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationResearchmaster Cognitive Neuroscienceen_US
dc.thesis.studyprogrammeResearchmaster Cognitive Neuroscienceen_US
dc.thesis.typeResearchmasteren_US
dc.titleComputation with spiking neural networksen_US
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