Phase transitions in neural networks

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dc.contributor.advisorRafelski, Johannen_ZA
dc.contributor.authorLittlewort, G Cen_ZA
dc.date.accessioned2014-09-22T07:56:59Z
dc.date.available2014-09-22T07:56:59Z
dc.date.issued1986en_ZA
dc.description.abstractThe behaviour of computer simulations of networks of neuron-like binary decision elements is studied. The models are discrete in time and deterministic , but the sequence of states of neurons in a net is not generally reversible in time because of the threshold nature of neurons. Self-organisation, or activity-dependent modification of interneuronal connection strengths, is used. Cyclic modes of activity which emerge spontaneously, underlie possible mechanisms of short term memory and associative thinking. The transition from seemingly random activity patterns to cyclic activity is examined in isolated networks with pseudorandomly chosen connection matrices; and the transition is related to the gross properties of the network. Nets with inherent structure (from pseudorandom nature) and imposed structure are studied, when cycles of length greater than, say, 12 time units are considered separately from the less complex, shorter cycles; the aforementioned transitions appear to be consistently rapid, compared to the cycle length, unless architecture is imposed such that nearly independent groups of neurons exist in the same net.en_ZA
dc.identifier.apacitationLittlewort, G. C. (1986). <i>Phase transitions in neural networks</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Physics. Retrieved from http://hdl.handle.net/11427/7617en_ZA
dc.identifier.chicagocitationLittlewort, G C. <i>"Phase transitions in neural networks."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Physics, 1986. http://hdl.handle.net/11427/7617en_ZA
dc.identifier.citationLittlewort, G. 1986. Phase transitions in neural networks. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Littlewort, G C AB - The behaviour of computer simulations of networks of neuron-like binary decision elements is studied. The models are discrete in time and deterministic , but the sequence of states of neurons in a net is not generally reversible in time because of the threshold nature of neurons. Self-organisation, or activity-dependent modification of interneuronal connection strengths, is used. Cyclic modes of activity which emerge spontaneously, underlie possible mechanisms of short term memory and associative thinking. The transition from seemingly random activity patterns to cyclic activity is examined in isolated networks with pseudorandomly chosen connection matrices; and the transition is related to the gross properties of the network. Nets with inherent structure (from pseudorandom nature) and imposed structure are studied, when cycles of length greater than, say, 12 time units are considered separately from the less complex, shorter cycles; the aforementioned transitions appear to be consistently rapid, compared to the cycle length, unless architecture is imposed such that nearly independent groups of neurons exist in the same net. DA - 1986 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 1986 T1 - Phase transitions in neural networks TI - Phase transitions in neural networks UR - http://hdl.handle.net/11427/7617 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/7617
dc.identifier.vancouvercitationLittlewort GC. Phase transitions in neural networks. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Physics, 1986 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/7617en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Physicsen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.titlePhase transitions in neural networksen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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