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The high recording channel counts, however, pose significant challenges for data analysis because the required time and computational resources grow superlinearly with the data volume. Herein, the feasibility of real\u2010time reconstruction of neural functional connectivity using a second\u2010order memristor network is analyzed. Spike\u2010timing\u2010dependent plasticity, natively implemented by the internal dynamics of the memristor device, leads to the successful discovery of temporal correlations between pre\u2010 and postsynaptic spikes of the simulated neural circuits in an unsupervised fashion. The proposed system demonstrates high classification accuracy under a wide range of parameter settings considering indirect connections, synaptic weights, transmission delays, connection density, and so on, and enables the capturing of dynamic connectivity evolutions. The influence of device nonideal factors on detection accuracy is systematically evaluated, and the system shows robustness to initial weight randomness, and cycle\u2010to\u2010cycle and device\u2010to\u2010device variations. The proposed method allows direct mapping of neural connectivity onto the artificial memristor network and can lead to efficient front\u2010end data analysis of high\u2010density neural recording systems and potentially directly coupled bioartificial networks.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202000276","type":"journal-article","created":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T11:05:24Z","timestamp":1621508724000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Neural Functional Connectivity Reconstruction with Second\u2010Order Memristor Network"],"prefix":"10.1002","volume":"3","author":[{"given":"Yuting","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science University of Michigan  Ann Arbor MI 48109 USA"}]},{"given":"John","family":"Moon","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and 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