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dc.contributor.authorTownsend, J
dc.contributor.authorKeedwell, EC
dc.contributor.authorGalton, A
dc.date.accessioned2016-03-31T09:15:18Z
dc.date.issued2013-08-03
dc.description.abstractSHRUTI is a model of how predicate relations can be represented and reasoned upon using a network of spiking neurons, attempting to model the brain’s ability to perform reasoning using as biologically plausible a means as possible. This paper extends the biological plausibility of the SHRUTI model by presenting a genotype representation of connections in a SHRUTI network using indirect encoding and showing that working networks represented in this way can be produced through an evolutionary process. A multi-objective algorithm is used to minimise the error and the number of weight changes that take place as a network learnsen_GB
dc.identifier.citationProceedings of the 9th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy13), pp. 56-61en_GB
dc.identifier.urihttp://hdl.handle.net/10871/20891
dc.language.isoenen_GB
dc.publisherNeural-Symbolic Learning and Reasoningen_GB
dc.relation.urlhttp://www.neural-symbolic.org/NeSy13/en_GB
dc.relation.urlhttp://daselab.cs.wright.edu/nesy/NeSy13/townsend.pdfen_GB
dc.rightsThis is the final version of the article. Available from Neural-Symbolic Learning and Reasoning via the link in this record.en_GB
dc.titleEvolution of connections in SHRUTI networksen_GB
dc.typeConference paperen_GB
dc.date.available2016-03-31T09:15:18Z


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