Dynamical Systems Theory for Transparent Symbolic Computation in Neuronal Networks
dc.contributor.supervisor | Rodrigues, Serafim | |
dc.contributor.author | Carmantini, Giovanni Sirio | |
dc.contributor.other | School of Engineering, Computing and Mathematics | en_US |
dc.date.accessioned | 2017-03-16T13:56:39Z | |
dc.date.available | 2017-03-16T13:56:39Z | |
dc.date.issued | 2017 | |
dc.date.issued | 2017 | |
dc.identifier | 10412380 | en_US |
dc.identifier.uri | http://hdl.handle.net/10026.1/8647 | |
dc.description.abstract |
In this thesis, we explore the interface between symbolic and dynamical system computation, with particular regard to dynamical system models of neuronal networks. In doing so, we adhere to a definition of computation as the physical realization of a formal system, where we say that a dynamical system performs a computation if a correspondence can be found between its dynamics on a vectorial space and the formal system’s dynamics on a symbolic space. Guided by this definition, we characterize computation in a range of neuronal network models. We first present a constructive mapping between a range of formal systems and Recurrent Neural Networks (RNNs), through the introduction of a Versatile Shift and a modular network architecture supporting its real-time simulation. We then move on to more detailed models of neural dynamics, characterizing the computation performed by networks of delay-pulse-coupled oscillators supporting the emergence of heteroclinic dynamics. We show that a correspondence can be found between these networks and Finite-State Transducers, and use the derived abstraction to investigate how noise affects computation in this class of systems, unveiling a surprising facilitatory effect on information transmission. Finally, we present a new dynamical framework for computation in neuronal networks based on the slow-fast dynamics paradigm, and discuss the consequences of our results for future work, specifically for what concerns the fields of interactive computation and Artificial Intelligence. | en_US |
dc.language.iso | en | |
dc.publisher | University of Plymouth | |
dc.rights | Attribution 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
dc.subject | Recurrent neural networks | |
dc.subject | Representation theory | |
dc.subject | Neural symbolic computation | |
dc.subject | Dynamical systems | |
dc.subject | Symbolic dynamics | |
dc.subject | Automata theory | en_US |
dc.subject.classification | PhD | en_US |
dc.title | Dynamical Systems Theory for Transparent Symbolic Computation in Neuronal Networks | en_US |
dc.type | Thesis | |
plymouth.version | publishable | en_US |
dc.identifier.doi | http://dx.doi.org/10.24382/1156 | |
dc.identifier.doi | http://dx.doi.org/10.24382/1156 | |
dc.rights.embargoperiod | No embargo | en_US |
dc.type.qualification | Doctorate | en_US |
rioxxterms.version | NA |
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