Oxygen Vacancy-Dependent Synaptic Dynamic Behavior of TiO<i>
                     <sub>x</sub>
                  </i>-Based Transparent Memristor

She, Yu; Wang, Fang; Zhao, Xuanyu; Zhang, Zhenzhong; Li, Chuang; Pan, Honggang; Hu, Kai; Zhang, Song; Zhang, Kailiang

doi:10.1109/ted.2021.3056333

Cited by 27 publications

(14 citation statements)

References 33 publications

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“…27 Exhibition of PPF, PPD, and PTP in an artificial synapse is associated with the generation of postsynaptic current (PSC) for a pair of input voltage pulses. 10,64,65 GP artificial synapses were also tested to measure PPF, PPD, and PTP properties. As shown in Figure 3c, when a pair of −5 V pulses with an interval of 5 ms and a width of 10 ms was applied to the device, the output current spikes were different in amplitude (I 2 > I 1 ) suggesting facilitation of the synapse.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…In biological synapses, a pair of presynaptic pulses can enhance or inhibit the second postsynaptic response, which is called PPF when enhanced or PPD when inhibited. ,, Furthermore, PTP is the enhancement of synaptic weight that lasts several minutes (i.e., a sort of extended PPF since it does not refer to the second output current but to a N output current value, compared to the first current value) . Exhibition of PPF, PPD, and PTP in an artificial synapse is associated with the generation of postsynaptic current (PSC) for a pair of input voltage pulses. ,, GP artificial synapses were also tested to measure PPF, PPD, and PTP properties. As shown in Figure c, when a pair of −5 V pulses with an interval of 5 ms and a width of 10 ms was applied to the device, the output current spikes were different in amplitude ( I 2 > I 1 ) suggesting facilitation of the synapse.…”

Section: Resultsmentioning

confidence: 99%

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Synaptic Properties of Geopolymer Memristors: Synaptic Plasticity, Spike-Rate-Dependent Plasticity, and Spike-Timing-Dependent Plasticity

Shakib,

Gao,

Lamuta

2023

ACS Appl. Electron. Mater.

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Memristors, also known as artificial synapses, are devices that are able to mimic the memory functions of biological synapses. To emulate synaptic functions, memristors need to exhibit plasticity, which is a pivotal phenomenon in their biological counterparts. In a previous work, we demonstrated that geopolymers present memristive properties. In this work, we study different types of synaptic plasticity properties of geopolymer memristors. We demonstrate short-term and long-term memory resulting from potentiation-depression; Hebbian learning inspired spike-timing-dependent plasticity, spike-rate-dependent plasticity, historydependent plasticity, paired-pulse facilitation, paired-pulse depression, and post-tetanic potentiation. These synaptic properties can be ascribed to the electro-osmosis-induced movement of ions in the capillaries and pores of the geopolymer memristors. These properties are extremely promising for the use of geopolymers in neuromorphic computing applications.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Synaptic Properties of Geopolymer Memristors: Synaptic Plasticity, Spike-Rate-Dependent Plasticity, and Spike-Timing-Dependent Plasticity

Shakib,

Gao,

Lamuta

2023

ACS Appl. Electron. Mater.

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“…It takes less time to remember forgotten information than the initial learning process, a phenomenon similar to the relearning behavior of the human brain. 48,49 The nervous system is constantly receiving large amounts of information from the outside world. Therefore, extracting useful information from large amounts of external information is a key task of synapses.…”

Section: Acs Sustainablementioning

confidence: 99%

Memristor Based on a Lotus Root/Lactalbumin Bilayer as a Biocompatible Artificial Synapse for Neuromorphic Electronics

Sun,

Yuan,

Wang

et al. 2023

ACS Sustainable Chem. Eng.

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Neuromorphic computing is a promising solution to overcome the bottleneck of the traditional von Neumann computing architecture and has attracted more and more attention. High-performance artificial synapses are fundamental components of brain-like chips, which are essential for efficient neuromorphic computing. This work reports the preparation of indium tin oxide/lotus root/lactalbumin/Al memristor artificial synapses. The lotus root and lactalbumin are the two active layers of the device. Protonation conduction in lactalbumin and oxygen vacancy conduction in the lotus root cause the device to exhibit synaptic function. The device successfully simulated the potentiation/depression properties of synapses, the transition from short-term memory to long-term memory, learn–forget–relearning behaviors, and spike timing-dependent plasticity. The device provides a possible implementation path for the realization of neuromorphic electronics based on the biomemristor.

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“…These behaviors are consistent with the ionic dynamic theory of the memristor switching process. [25][26][27]…”

Section: Dynamic Memristor-based Artificial Dendrite Devicementioning

confidence: 99%

A Memristors‐Based Dendritic Neuron for High‐Efficiency Spatial‐Temporal Information Processing

et al. 2022

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Diverse microscopic ionic dynamics help mediate the ability of a biological neural network to handle complex tasks with low energy consumption. Thus, rich internal ionic dynamics in memristors based on transition metal oxide are expected to provide a unique and useful platform for implementing energy‐efficient neuromorphic computing. To this end, a titanium oxide (TiOx)‐based interface‐type dynamic memristor and an niobium oxide (NbOx)‐based Mott memristor are integrated as an artificial dendrite and spike‐firing soma, respectively, to construct a dendritic neuron unit for realizing high‐efficiency spatial‐temporal information processing. Further, a dendritic neural network is hardware‐implemented for spatial‐temporal information processing to highlight the computational advantages achieved by incorporating dendritic functions in the network. Human motion recognition is demonstrated using the Nanyang Technological University‐Red Green Blue (NTU‐RGB) dataset as a benchmark spatial‐temporal task; it shows a nearly 20% improvement in accuracy for the memristors‐based hardware incorporating dendrites and a 1000× advantage in power efficiency compared to that of the graphics processing unit (GPU). The dendritic neuron developed in this study can be considered a critical building block for implementing more bio‐plausible neural networks that can manage complex spatial‐temporal tasks with high efficiency.

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Oxygen Vacancy-Dependent Synaptic Dynamic Behavior of TiO _x -Based Transparent Memristor

Cited by 27 publications

References 33 publications

Synaptic Properties of Geopolymer Memristors: Synaptic Plasticity, Spike-Rate-Dependent Plasticity, and Spike-Timing-Dependent Plasticity

Synaptic Properties of Geopolymer Memristors: Synaptic Plasticity, Spike-Rate-Dependent Plasticity, and Spike-Timing-Dependent Plasticity

Memristor Based on a Lotus Root/Lactalbumin Bilayer as a Biocompatible Artificial Synapse for Neuromorphic Electronics

A Memristors‐Based Dendritic Neuron for High‐Efficiency Spatial‐Temporal Information Processing

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Oxygen Vacancy-Dependent Synaptic Dynamic Behavior of TiO x -Based Transparent Memristor

Cited by 27 publications

References 33 publications

Synaptic Properties of Geopolymer Memristors: Synaptic Plasticity, Spike-Rate-Dependent Plasticity, and Spike-Timing-Dependent Plasticity

Synaptic Properties of Geopolymer Memristors: Synaptic Plasticity, Spike-Rate-Dependent Plasticity, and Spike-Timing-Dependent Plasticity

Memristor Based on a Lotus Root/Lactalbumin Bilayer as a Biocompatible Artificial Synapse for Neuromorphic Electronics

A Memristors‐Based Dendritic Neuron for High‐Efficiency Spatial‐Temporal Information Processing

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Oxygen Vacancy-Dependent Synaptic Dynamic Behavior of TiO _x -Based Transparent Memristor