| Conţinutul numărului revistei |
| Articolul precedent |
| Articolul urmator |
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 SM ISO690:2012WU, Tingfang, PAN, Linqiang, ALHAZOV, Artiom. Computation power of asynchronous spiking neural P systems with polarizations. In: Theoretical Computer Science, 2019, nr. 777, pp. 474-489. ISSN 0304-3975. DOI: https://doi.org/10.1016/j.tcs.2018.10.024 |
| EXPORT metadate: Google Scholar Crossref CERIF DataCite Dublin Core |
  Theoretical Computer Science |
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| Numărul 777 / 2019 / ISSN 0304-3975 /ISSNe 1879-2294 | ||||||
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| DOI:https://doi.org/10.1016/j.tcs.2018.10.024 | ||||||
| Pag. 474-489 | ||||||
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| Rezumat | ||||||
Spiking neural P systems (SN P systems) are a class of parallel computing models, inspired by the way in which neurons process information and communicate to each other by means of spikes. In this work, we consider a variant of SN P systems, SN P systems with polarizations (PSN P systems), where the integrate-and-fire conditions are associated with polarizations of neurons. The computation power of PSN P systems working in the asynchronous mode (at a computation step, a neuron with enabled rules does not obligatorily fire), instead of the synchronous mode (a neuron with enabled rules should fire), is investigated. We proved that asynchronous PSN P systems with extended rules (the application of a rule can produce more than one spikes) or standard rules (all rules can only produce a spike) can both characterize partially blind counter machines, hence, such systems are not Turing universal. The equivalence of the computation power of asynchronous PSN P systems in both cases of using extended rules or standard rules indicates that asynchronous PSN P systems are robust in terms of the amount of information exchanged among neurons. It is known that synchronous PSN P systems with standard rules are Turing universal, so these results also suggest that the working model, synchronization or asynchronization, is an essential ingredient for a PSN P system to achieve a powerful computation capability. |
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| Cuvinte-cheie Asynchronization, Bio-inspired computing, Membrane computing, Spiking neural network, Spiking neural P system |
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Dublin Core Export
<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc='http://purl.org/dc/elements/1.1/' xmlns:oai_dc='http://www.openarchives.org/OAI/2.0/oai_dc/' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xsi:schemaLocation='http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd'> <dc:creator>Wu, T.</dc:creator> <dc:creator>Pan, L.</dc:creator> <dc:creator>Alhazov, A.E.</dc:creator> <dc:date>2019-07-19</dc:date> <dc:description xml:lang='en'><p>Spiking neural P systems (SN P systems) are a class of parallel computing models, inspired by the way in which neurons process information and communicate to each other by means of spikes. In this work, we consider a variant of SN P systems, SN P systems with polarizations (PSN P systems), where the integrate-and-fire conditions are associated with polarizations of neurons. The computation power of PSN P systems working in the asynchronous mode (at a computation step, a neuron with enabled rules does not obligatorily fire), instead of the synchronous mode (a neuron with enabled rules should fire), is investigated. We proved that asynchronous PSN P systems with extended rules (the application of a rule can produce more than one spikes) or standard rules (all rules can only produce a spike) can both characterize partially blind counter machines, hence, such systems are not Turing universal. The equivalence of the computation power of asynchronous PSN P systems in both cases of using extended rules or standard rules indicates that asynchronous PSN P systems are robust in terms of the amount of information exchanged among neurons. It is known that synchronous PSN P systems with standard rules are Turing universal, so these results also suggest that the working model, synchronization or asynchronization, is an essential ingredient for a PSN P system to achieve a powerful computation capability.</p></dc:description> <dc:identifier>10.1016/j.tcs.2018.10.024</dc:identifier> <dc:source>Theoretical Computer Science (777) 474-489</dc:source> <dc:subject>Asynchronization</dc:subject> <dc:subject>Bio-inspired computing</dc:subject> <dc:subject>Membrane computing</dc:subject> <dc:subject>Spiking neural network</dc:subject> <dc:subject>Spiking neural P system</dc:subject> <dc:title>Computation power of asynchronous spiking neural P systems with polarizations</dc:title> <dc:type>info:eu-repo/semantics/article</dc:type> </oai_dc:dc>
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