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More precisely, the integrate-and-fire behavior of neurons is achieved by spike-evolution rules, and the distribution of produced spikes is achieved by spike-communication rules. Then, the computational power of ECSNP systems is examined. It is demonstrated that ECSNP systems are Turing universal as number-generating devices. Furthermore, the computational power of ECSNP systems with a restricted form, i.e. the quantity of spikes in each neuron throughout a computation does not exceed some constant, is also investigated, and it is shown that such restricted ECSNP systems can only characterize the family of semilinear number sets. These results manifest that the capacity of neurons for information storage (i.e. the quantity of spikes) has a critical impact on the ECSNP systems to achieve a desired computational power. <\/jats:p>","DOI":"10.1142\/s0129065720500641","type":"journal-article","created":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T04:10:11Z","timestamp":1604895011000},"page":"2050064","source":"Crossref","is-referenced-by-count":25,"title":["Evolution-Communication Spiking Neural P Systems"],"prefix":"10.1142","volume":"31","author":[{"given":"Tingfang","family":"Wu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215006, P. R. China"},{"name":"Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, P. R. China"}]},{"given":"Qiang","family":"Lyu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215006, P. R. 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