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      Mimicking the brain functions of learning, forgetting and explicit/implicit memories with SrTiO3-based memristive devices

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          Abstract

          Brain functions are performed by a complex neural system consisting of a network of gigantic amounts of neurons (∼10 11) and synapses (∼10 15); in this work, the brain functions of learning, forgetting and explicit/implicit memory are successfully mimicked using Ni/Nb-SrTiO 3/Ti memristive devices.

          Abstract

          To implement the complex brain functions of learning, forgetting and memory in a single electronic device is very advantageous for realizing artificial intelligence. As a proof of concept, memristive devices with a simple structure of Ni/Nb-SrTiO 3/Ti were investigated in this work. The functions of learning, forgetting and memory were successfully mimicked using the memristive devices, and the “time-saving” effect of implicit memory was also demonstrated. The physics behind the brain functions is simply the modulation of the Schottky barrier at the Ni/SrTiO 3 interface. The realization of various psychological functions in a single device simplifies the construction of the artificial neural network and facilitates the advent of artificial intelligence.

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          Nanoscale memristor device as synapse in neuromorphic systems.

          A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. Here we experimentally demonstrate a nanoscale silicon-based memristor device and show that a hybrid system composed of complementary metal-oxide semiconductor neurons and memristor synapses can support important synaptic functions such as spike timing dependent plasticity. Using memristors as synapses in neuromorphic circuits can potentially offer both high connectivity and high density required for efficient computing.
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            Resistive switching in transition metal oxides

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              Short-term plasticity and long-term potentiation mimicked in single inorganic synapses.

              Memory is believed to occur in the human brain as a result of two types of synaptic plasticity: short-term plasticity (STP) and long-term potentiation (LTP; refs 1-4). In neuromorphic engineering, emulation of known neural behaviour has proven to be difficult to implement in software because of the highly complex interconnected nature of thought processes. Here we report the discovery of a Ag(2)S inorganic synapse, which emulates the synaptic functions of both STP and LTP characteristics through the use of input pulse repetition time. The structure known as an atomic switch, operating at critical voltages, stores information as STP with a spontaneous decay of conductance level in response to intermittent input stimuli, whereas frequent stimulation results in a transition to LTP. The Ag(2)S inorganic synapse has interesting characteristics with analogies to an individual biological synapse, and achieves dynamic memorization in a single device without the need of external preprogramming. A psychological model related to the process of memorizing and forgetting is also demonstrated using the inorganic synapses. Our Ag(2)S element indicates a breakthrough in mimicking synaptic behaviour essential for the further creation of artificial neural systems that emulate characteristics of human memory.
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                Author and article information

                Journal
                PPCPFQ
                Physical Chemistry Chemical Physics
                Phys. Chem. Chem. Phys.
                Royal Society of Chemistry (RSC)
                1463-9076
                1463-9084
                2016
                2016
                : 18
                : 46
                : 31796-31802
                Affiliations
                [1 ]Laboratory of Solid State Ionics
                [2 ]School of Materials Science and Engineering
                [3 ]Huazhong University of Science and Technology
                [4 ]Wuhan 430074
                [5 ]P. R. China
                [6 ]Wuhan National Laboratory for Optoelectronics
                [7 ]School of Optical and Electronic Information
                Article
                10.1039/C6CP06049H
                27841389
                70bac5de-8251-41b1-88ef-c54102488986
                © 2016
                History

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