Stack- and Queue-like Dynamics in Recurrent Neural Networks
Grüning, A (2006) Stack- and Queue-like Dynamics in Recurrent Neural Networks Connection Science, 18. 23 - 42. ISSN 0954-0091
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What dynamics do simple recurrent networks (SRNs) develop to represent stack-like and queue-like memories? SRNs have been widely used as models in cognitive science. However, they are interesting in their own right as non-symbolic computing devices from the viewpoints of analogue computing and dynamical systems theory. In this paper, SRNs are trained oil two prototypical formal languages with recursive structures that need stack-like or queue-like memories for processing, respectively. The evolved dynamics are analysed, then interpreted in terms of simple dynamical systems, and the different ease with which SRNs aquire them is related to the properties of these simple dynamical Within the dynamical systems framework, it is concluded that the stack-like language is simpler than the queue-like language, without making use of arguments from symbolic computation theory.
|Divisions :||Faculty of Engineering and Physical Sciences > Computing Science|
|Identification Number :||10.1080/09540090500317291|
|Uncontrolled Keywords :||simple current networks, network dynamics, formal languages, dynamical complexity, CONTEXT-FREE, ARCHITECTURAL BIAS, LANGUAGE, TIME, COMPUTATION, RECOGNIZERS, EXTRACTION, INDUCTION, MACHINES, TAXONOMY|
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|Additional Information :||This is an electronic version of an article published in Connection Science, 18(1), 23-42(2006). Connection Science is available online at: http://www.tandfonline.com/doi/abs/10.1080/09540090500317291.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||07 Dec 2011 14:33|
|Last Modified :||23 Sep 2013 18:53|
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