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Phase transitions in neural networks
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,...
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| Format: | Thesis |
| Language: | English |
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Department of Physics
2014
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| _version_ | 1867614226753781760 |
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| access_status_str | Open Access |
| author | Littlewort, G C |
| author2 | Rafelski, Johann |
| author_browse | Littlewort, G C Rafelski, Johann |
| author_facet | Rafelski, Johann Littlewort, G C |
| author_sort | Littlewort, G C |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/7617 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:48:41.010Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | Department of Physics |
| publisherStr | Department of Physics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/7617 Phase transitions in neural networks Littlewort, G C Rafelski, Johann 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. 2014-09-22T07:56:59Z 2014-09-22T07:56:59Z 1986 Master Thesis Masters MSc http://hdl.handle.net/11427/7617 eng application/pdf Department of Physics Faculty of Science University of Cape Town |
| spellingShingle | Littlewort, G C Phase transitions in neural networks |
| thesis_degree_str | Master's |
| title | Phase transitions in neural networks |
| title_full | Phase transitions in neural networks |
| title_fullStr | Phase transitions in neural networks |
| title_full_unstemmed | Phase transitions in neural networks |
| title_short | Phase transitions in neural networks |
| title_sort | phase transitions in neural networks |
| url | http://hdl.handle.net/11427/7617 |
| work_keys_str_mv | AT littlewortgc phasetransitionsinneuralnetworks |