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Memristor is known to be a fundamental block to generate complex behaviors. It also is reported to be able to emulate synaptic long\u2010term plasticity as well as short\u2010term plasticity. Synaptic plasticity is one of the important foundations of learning and memory as the high\u2010order functional properties of the brain. In this study, it is shown that memristive neuronal network can represent plasticity phenomena observed in biological cortical synapses. A network of neuronal units as a two\u2010dimensional excitable tissue is designed with 3\u2010neuron Hopfield neuronal model for the local dynamics of each unit. The results show that the lattice supports spatiotemporal pattern formation without supervision. It is found that memristor\u2010type coupling is more noticeable against resistor\u2010type coupling, while determining the excitable tissue switch over different complex behaviors. The stability of the resulting spatiotemporal patterns against noise is studied as well. Finally, the bifurcation analysis is carried out for variation of memristor effect. Our study reveals that the spatiotemporal electrical activity of the tissue concurs with the bifurcation analysis. It is shown that the memristor coupling intensities, by which the system undergoes periodic behavior, prevent the tissue from holding wave propagation. Besides, the chaotic behavior in bifurcation diagram corresponds to turbulent spatiotemporal behavior of the tissue. Moreover, we found that the excitable media are very sensitive to noise impact when the neurons are set close to their bifurcation point, so that the respective spatiotemporal pattern is not stable.<\/jats:p>","DOI":"10.1155\/2018\/6427870","type":"journal-article","created":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T23:41:09Z","timestamp":1530142869000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Investigation of Cortical Signal Propagation and the Resulting Spatiotemporal Patterns in Memristor\u2010Based Neuronal Network"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5090-1139","authenticated-orcid":false,"given":"Ke","family":"Ding","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zahra","family":"Rostami","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sajad","family":"Jafari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2638-3463","authenticated-orcid":false,"given":"Boshra","family":"Hatef","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,6,27]]},"reference":[{"key":"e_1_2_7_1_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0217979216502519"},{"key":"e_1_2_7_2_2","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/4631602"},{"volume-title":"Principles of Neural Science","year":"2000","author":"Kandel E. 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