| 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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