dc.contributor.author Schliebs, S
dc.contributor.author Mohemmed, A
dc.contributor.author Kasabov, N
dc.date.accessioned 2011-08-04T03:13:53Z
dc.date.available 2011-08-04T03:13:53Z
dc.date.copyright 2011-07-31
dc.date.issued 2011-08-04
dc.identifier.citation 2011 International Joint Conference on Neural Networks, San Jose, California, July 31 - August 5, 2011
dc.identifier.uri http://hdl.handle.net/10292/1570
dc.description.abstract This study employs networks of stochastic spiking neurons as reservoirs for liquid state machines (LSM). We experimentally investigate the separation property of these reservoirs and show their ability to generalize classes of input signals. Similar to traditional LSM, probabilistic LSM (pLSM) have the separation property enabling them to distinguish between different classes of input stimuli. Furthermore, our results indicate some potential advantages of non-deterministic LSM by improving upon the separation ability of the liquid. Three non-deterministic neural models are considered and for each of them several parameter configurations are explored. We demonstrate some of the characteristics of pLSM and compare them to their deterministic counterparts. pLSM offer more flexibility due to the probabilistic parameters resulting in a better performance for some values of these parameters.
dc.publisher AUT University
dc.relation.isreplacedby 10292/1652
dc.relation.isreplacedby http://hdl.handle.net/10292/1652
dc.title Are probabilistic spiking neural networks suitable for reservoir computing?
dc.type Conference Contribution
dc.rights.accessrights OpenAccess

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