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dc.contributor.authorChoo, Feng-Xuan 18:51:25 (GMT) 18:51:25 (GMT)
dc.description.abstractIn a world dominated by temporal order, memory capable of processing, encoding and subsequently recalling ordered information is very important. Over the decades this memory, known as serial memory, has been extensively studied, and its effects are well known. Many models have also been developed, and while these models are able to reproduce the behavioural effects observed in human recall studies, they are not always implementable in a biologically plausible manner. This thesis presents the Ordinal Serial Encoding model, a model inspired by biology and designed with a broader view of general cognitive architectures in mind. This model has the advantage of simplicity, and we show how neuro-plausibility can be achieved by employing the principles of the Neural Engineering Framework in the model’s design. Additionally, we demonstrate that not only is the model able to closely mirror human performance in various recall tasks, but the behaviour of the model is itself a consequence of the underlying neural architecture.en
dc.publisherUniversity of Waterlooen
dc.subjectserial memoryen
dc.subjectneural engineering frameworken
dc.subjectspiking neuron modelen
dc.subjectworking memoryen
dc.subjectserial-order recallen
dc.titleThe Ordinal Serial Encoding Model: Serial Memory in Spiking Neuronsen
dc.typeMaster Thesisen
dc.subject.programComputer Scienceen of Computer Scienceen
uws-etd.degreeMaster of Mathematicsen

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