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Journal ArticleDOI

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

TLDR
Memory devices with a simple structure of Ni/Nb-SrTiO3/Ti were investigated and 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.
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-SrTiO3/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/SrTiO3 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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Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

TL;DR: The progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics, and future research directions toward wearable artificial neuromorphic systems are suggested.
Journal ArticleDOI

Nanoionics-Enabled Memristive Devices: Strategies and Materials for Neuromorphic Applications

TL;DR: A critical overview of the proposed nano-ionic mechanisms for memristive switching is given in this paper, focusing particularly on providing fundamental insights into the strategies for regulating the adaptive memrisive characteristics of devices that resemble the behaviors of biological synapses, which is an element of neural networks.
Journal ArticleDOI

Memristive Synapses and Neurons for Bioinspired Computing

TL;DR: Approaches to realize certain synaptic or neuronal functions are introduced with state‐of‐art experimental demonstrations and approaches to realize the important learning rules, like spiking‐timing‐dependent plasticity and Bienenstock–Cooper–Munro learning rules are elaborated according to the level of faithfulness to biological synapses.
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Quasi-Hodgkin-Huxley Neurons with Leaky Integrate-and-Fire Functions Physically Realized with Memristive Devices.

TL;DR: In this paper, quasi-Huxley neurons with leaky integrate-and-fire functions are physically demonstrated with a volatile memristive device, W/WO3 /poly(3,4-ethylenedioxythiophene): polystyrene sulfonate/Pt.
References
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Journal ArticleDOI

Nanoscale Memristor Device as Synapse in Neuromorphic Systems

TL;DR: A nanoscale silicon-based memristor device is experimentally demonstrated and it is shown 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.
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Memristive switching mechanism for metal/oxide/metal nanodevices.

TL;DR: Experimental evidence is provided to support this general model of memristive electrical switching in oxide systems, and micro- and nanoscale TiO2 junction devices with platinum electrodes that exhibit fast bipolar nonvolatile switching are built.
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Resistive switching in transition metal oxides

TL;DR: In this paper, the authors review the current status of one of the alternatives, resistance random access memory (ReRAM), which uses a resistive switching phenomenon found in transition metal oxides.
Book

Memory; A Contribution to Experimental Psychology

TL;DR: The first scientific text on the psychology of memory, Hermann Ebbinghaus extended the province of systematic, experimental research to the higher mental processes.
Journal ArticleDOI

Training and operation of an integrated neuromorphic network based on metal-oxide memristors

TL;DR: The experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification).
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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.