| dc.contributor.author | Calcraft, L. | |
| dc.contributor.author | Adams, R.G. | |
| dc.contributor.author | Chen, W. | |
| dc.contributor.author | Davey, N. | |
| dc.date.accessioned | 2008-10-01T13:57:09Z | |
| dc.date.available | 2008-10-01T13:57:09Z | |
| dc.date.issued | 2008 | |
| dc.identifier.citation | Calcraft , L , Adams , R G , Chen , W & Davey , N 2008 , Using graph theoretic measures to predict the performance of associative memory models . in ESANN2008: 16th European Symposium on Artificial Neural Networks . ESANN , pp. 107-112 . | |
| dc.identifier.isbn | 2-930-307080 | |
| dc.identifier.other | PURE: 84126 | |
| dc.identifier.other | PURE UUID: ed66b94c-c638-4452-926f-4eba5bb00e07 | |
| dc.identifier.other | dspace: 2299/2415 | |
| dc.identifier.other | Scopus: 84887014605 | |
| dc.identifier.uri | http://hdl.handle.net/2299/2415 | |
| dc.description | Original paper can be found at: http://www.dice.ucl.ac.be/esann/proceedings/electronicproceedings.htm | |
| dc.description.abstract | We test a selection of associative memory models built with different connection strategies, exploring the relationship between the structural properties of each network and its pattern-completion performance. It is found that the Local Efficiency of the network can be used to predict pattern completion performance for associative memory models built with a range of different connection strategies. This relationship is maintained as the networks are scaled up in size, but breaks down under conditions of very sparse connectivity. | en |
| dc.language.iso | eng | |
| dc.publisher | ESANN | |
| dc.relation.ispartof | ESANN2008: 16th European Symposium on Artificial Neural Networks | |
| dc.title | Using graph theoretic measures to predict the performance of associative memory models | en |
| dc.contributor.institution | School of Computer Science | |
| dc.contributor.institution | Science & Technology Research Institute | |
| rioxxterms.type | Other | |
| herts.preservation.rarelyaccessed | true | |
