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Electronic imitation of behavioral and psychological synaptic activities by the TiOx/Al2O3-based memristor devices


Seeking an effective electronic synapse to emulate the biological synaptic behavior is the fundamental need of building the brain-inspired computer. Recently, an emerging two-terminal memristor, in which conductance can be gradually modulated by external electrical stimuli, is widely concerned as the strongest competitor of the electronic synapse. Here, we show the capabilities to imitate the synaptic behaviors by using TiOx/Al2O3-based memristor devices. Along with the analog resistive switching performances, the devices are replicating the bio-synapses behaviors of potentiation/depression, short-term-plasticity, long-term-potentiation, which is giving the glimpses of the usefulness of TiOx/Al2O3-based memristor as an electronic synapse. The essential memorizing capabilities of the brain are dependent on the connection strength between neurons and the memory types are changed from short-term memory to long-term memory. In the TiOx/Al2O3-based electronic synaptic junction, the memorizing levels can change its state with standard rehearsal process and also with newly introduced process “impact of event” i.e. the impact of pulse amplitude, and width of the input pulse. The devices are showing a short-term to long-term memory effect with introducing intermediate mezzanine memory. The experimental achievements by TiOx/Al2O3 electronic synapses are finally psychologically modeled with considering the mezzanine level. It is highly recommended to investigate the similar phenomena for the other memristor systems to check the authenticity of this model.

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Supplementary files

Publication details

The article was received on 03 Jul 2017, accepted on 05 Sep 2017 and first published on 06 Sep 2017

Article type: Paper
DOI: 10.1039/C7NR04741J
Citation: Nanoscale, 2017, Accepted Manuscript
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    Electronic imitation of behavioral and psychological synaptic activities by the TiOx/Al2O3-based memristor devices

    W. Banerjee, Q. Liu, H. Lv, S. Long and M. Liu, Nanoscale, 2017, Accepted Manuscript , DOI: 10.1039/C7NR04741J

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