caa a22 4500 606158936 CHVBK 20210128100622.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00521-014-1697-7 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1697-7 Nonlinear behavior of memristive devices during tuning process and its impact on STDP learning rule in memristive neural networks [Elektronische Daten] [Farnood Merrikh Bayat, Saeed Shouraki] It is now widely accepted that memristive devices are promising candidates for the emulation of the behavior of biological synapses in neuromorphic systems. This is mainly because of the fact that like the strength of synapse, memristance of the memristive device can be tuned actively for example by the application of voltage or current. In addition, it is also possible to fabricate high density of memristive devices through the nano-crossbar structures. In this paper, we will show that there are some problems associated with memristive devices, which are playing the role of biological synapses. For example, we show that the variation rate of the memristance depends completely on the initial state of the device, and therefore, it can change significantly with time during the learning phase. This phenomenon can degrade the performance of learning methods like spike timing-dependent plasticity and cause the corresponding neuromorphic systems to become unstable. We also illustrate that using two serially connected memristive devices with different polarities as a synapse can somewhat fix the aforementioned problem. The Natural Computing Applications Forum, 2014 Memristive device nationallicence Synapse nationallicence Hebbian learning nationallicence Spike timing-dependent plasticity nationallicence Neuromorphic systems nationallicence Merrikh Bayat Farnood Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran aut Shouraki Saeed Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran aut Neural Computing and Applications Springer London 26/1(2015-01-01), 67-75 0941-0643 26:1<67 2015 26 521 https://doi.org/10.1007/s00521-014-1697-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1697-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Merrikh Bayat Farnood Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran aut NATIONALLICENCE 700 1- Shouraki Saeed Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/1(2015-01-01), 67-75 0941-0643 26:1<67 2015 26 521