Understanding and Modifying Dynamical Hopfield Neural Networks for Generating Multiple Coherent Patterns

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dc.contributor.advisor Fourrier, N en
dc.contributor.advisor Taylor, S en
dc.contributor.author Kuo, Nicholas en
dc.date.accessioned 2017-08-07T01:47:31Z en
dc.date.issued 2017 en
dc.identifier.uri http://hdl.handle.net/2292/34849 en
dc.description Full text is available to authenticated members of The University of Auckland only. en
dc.description.abstract Analysts have always been using classifications and predictions to deepen our understandings of the world we live in; and it is a fact that there has been a surge in the amount of data in the world with advancements in the internet and information management. For this reason, case-by-case qualitative analyses become inefficient and inevitably we start to rely on quantitative analyses. Quantitative analyses such as machine learning and deep learning have seen a lot success in industries; however they have been referred as black boxes because of their complexities. In this thesis, we aim to make one archetype of quantitative analytical technique, the recurrent dynamical Hopfield neural networks (RDHNN), transparent. We conduct rigorous experimental studies on Hopfield neural networks with David Sussillo’s FORCE mechanism via mathematical dynamical bifurcational studies. We compare his ideas with well-studied low dimensional systems and provide mathematical reconstructions and visualisations of the interactions among the network variables and the attractors (destinations of propagating in- formation). The result of this thesis is a computational scheme for RDHNNs with FORCE mechanism that can theoretically learn as many signals as possible at the same time. We provide the outputs for our final modification to show the successful simultaneous learning of four desired signals without human interference. We also show that our findings have very good forecastability with real life time series data. en
dc.publisher ResearchSpace@Auckland en
dc.relation.ispartof Masters Thesis - University of Auckland en
dc.relation.isreferencedby UoA99265060313102091 en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights Restricted Item. Available to authenticated members of The University of Auckland. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/nz/ en
dc.title Understanding and Modifying Dynamical Hopfield Neural Networks for Generating Multiple Coherent Patterns en
dc.type Thesis en
thesis.degree.discipline Applied Mathematics en
thesis.degree.grantor The University of Auckland en
thesis.degree.level Masters en
dc.rights.holder Copyright: The author en
pubs.elements-id 644931 en
pubs.record-created-at-source-date 2017-08-07 en

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http://creativecommons.org/licenses/by-nc-sa/3.0/nz/ Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-sa/3.0/nz/


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