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An optimum circuit configuration demonstrated multilevel stable resistive states, which are analogous to the connection weights in the human synapse. In order to modulate these memristive weights for representing the learning activities in human brain synapse, it was identified that the pulse width modulation technique is superior as compared to spike-timing-dependent plasticity. Further, the above analysis was utilized in training the memristor to update its resistive weights in consonance with its learning, analogous to that in a neural network. Further, the memristive crossbar architecture was utilized to implement a real-time application in Econometrics, where an array of memristors were utilized to learn and update the purchase trends of an [Formula: see text] matrix of customers. The proposed circuits possess the advantages of high packing density, low power consumption and nonvolatility, and also pave the way for developing future neuromorphic circuits. <\/jats:p>","DOI":"10.1142\/s0218126619502013","type":"journal-article","created":{"date-parts":[[2018,11,23]],"date-time":"2018-11-23T02:15:00Z","timestamp":1542939300000},"page":"1950201","source":"Crossref","is-referenced-by-count":9,"title":["Programming of Memristive Artificial Synaptic Crossbar Network Using PWM Techniques"],"prefix":"10.1142","volume":"28","author":[{"given":"P. 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