Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01hm50tv16g
 Title: Dynamics of Recurrent Neural Network Models of Working Memory Authors: Wawrzonek, Christian Advisors: Buschman, Timothy Department: Computer Science Class Year: 2016 Abstract: How do populations of neurons encode information over very short time scales? Given extensive training, neurons are able to change the weighted connections between them in order to encode information. However, very short timescales of only a few seconds are far too short to change neural weights. Still, humans and higher functioning animals pos- sess the ability to encode and maintain small amounts of information presented over very short timescales. This is the problem of working memory, the transient holding, processing, and manipulation of information used in higher cognitive functioning. Previous computa- tional models of working memory have typically been constructed with a strict, hand-tuned architecture. Here, we attempted to train a relatively simple, unconstrained neural network on complex working memory tasks and analyzed the natural solution space found by the network. Through a range of analyses, it is clear that even a simple, single layer recurrent network is capable of dynamic, generalized solutions without deliberate solution paths presented [8]. Extent: 41 pages URI: http://arks.princeton.edu/ark:/88435/dsp01hm50tv16g Type of Material: Princeton University Senior Theses Language: en_US Appears in Collections: Computer Science, 1988-2016

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