The close replication of synaptic functions is an important objective for achieving a highly realistic memristor-based cognitive computation. The emulation of neurobiological learning rules may allow the development of neuromorphic systems that continuously learn without supervision. In this work, the Bienenstock-Cooper-Munro learning rule, as a typical case of spike-rate-dependent plasticity, is mimicked using a generalized triplet-spike-timingdependent plasticity scheme in a WO 3−x memristive synapse. It demonstrates both presynaptic and postsynaptic activities and remedies the absence of the enhanced depression effect in the depression region, allowing a better description of the biological counterpart. The threshold sliding effect of Bienenstock-Cooper-Munro rule is realized using a historydependent property of the second-order memristor. Rate-based orientation selectivity is demonstrated in a simulated feedforward memristive network with this generalized Bienenstock-Cooper-Munro framework. These findings provide a feasible approach for mimicking Bienenstock-Cooper-Munro learning rules in memristors, and support the applications of spatiotemporal coding and learning using memristive networks.
Exploration of optoelectronic memristors with the capability to combine sensing and processing functions is required to promote development of efficient neuromorphic vision. In this work, the authors develop a plasmonic optoelectronic memristor that relies on the effects of localized surface plasmon resonance (LSPR) and optical excitation in an Ag–TiO2 nanocomposite film. Fully light‐induced synaptic plasticity (e.g., potentiation and depression) under visible and ultraviolet light stimulations is demonstrated, which enables the functional combination of visual sensing and low‐level image pre‐processing (including contrast enhancement and noise reduction) in a single device. Furthermore, the light‐gated and electrically‐driven synaptic plasticity can be performed in the same device, in which the spike‐timing‐dependent plasticity (STDP) learning functions can be reversibly modulated by visible and ultraviolet light illuminations. Thereby, the high‐level image processing function, i.e., image recognition, can also be performed in this memristor, whose recognition rate and accuracy are obviously enhanced as a result of image pre‐processing and light‐gated STDP enhancement. Experimental analysis shows that the memristive switching mechanism under optical stimulation can be attributed to the oxidation/reduction of Ag nanoparticles due to the effects of LSPR and optical excitation. The authors' work proposes a new type of plasmonic optoelectronic memristor with fully light‐modulated capability that may promote the future development of efficient neuromorphic vision.
features beyond mechanical flexibility such as wearability, stretchability, portability, and biocompatibility. For instance, Chen's group successfully demonstrated stretchable motion memory devices based on mechanical hybrid materials, which can work in the wearable state. [25] In order to achieve those properties, the nonconventional substrates are usually necessary to integrate with the devices, such as flexible organics, biocompatible polymer, and nonplanar substrates. However, these substrates may possess poor tolerance for high temperature during the device fabrication. In general, there exist two approaches to obtain functional devices on the arbitrary nonconventional substrates. On the one hand, as the reviewer mentioned, the flexible organic devices can be directly fabricated on these desired substrates due to the low-temperature processing of organic materials. [26][27][28][29] On the other hand, fabrication of transferable or free-standing devices is a more universal method for the integration with nonconventional substrates, which is applicable not only to organic materials but also to inorganic materials. [30][31][32][33] As a matter of fact, fabrication of inorganic devices on nonconventional substrates is usually faced with some difficulties. For example, the thermal processing, which is necessary in some cases to obtain high-quality films, may limit the use of organic and biocompatible polymer substrates; the shadow effect of physical deposition (e.g., sputtering or pulsed laser deposition), which is usually employed to deposit inorganic films, can result in the film nonuniformity when they are fabricated on nonplanar substrates. Thus, the development of transferable and free-standing electronic devices can well protect the functional substrates from harsh processing, and also, the transferable devices can be stuck conformally onto the desired substrates (like 3D-curve or folded) to realize future applications on wearable computers, epidermal electronics, and implantable chips. For example, Wan et al. have successfully demonstrated free-standing artificial synapses using 3D protoncoupled transistors on chitosan membranes. [33] Recently, two-terminal memristors have been proposed as one promising candidate for the artificial synapses thanks to its variable conductance in analogy with the change of synapse weight. [34][35][36][37][38] A variety of materials, such as metal oxides, [34][35][36] chalcogenide, [37] Si, [38] Perovskite, [39,40] and organics, [41] have been employed as building blocks of memristor-based artificial synapses. Diverse synaptic functions atThe absence of an effective approach to achieve free-standing inorganic memristors seriously hinders the development of transferable artificial synapses. Here, a transferable WO x -based memristive synapse is demonstrated using a nondestructive water-dissolution method in which the NaCl substrate is selected as the sacrificial layer due to its thermotolerance and water-solubility. The essential synaptic learning functions are achieved to com...
An all-carbon memristive synapse is highly desirable for hardware implementation in future wearable neuromorphic computing systems. Graphene oxide (GO) can exhibit resistive switching (RS) and may be a feasible candidate to achieve this objective. However, the digital-type RS often occurring in GO-based memristors restricts the biorealistic emulation of synaptic functions. Here, an all-carbon memristive synapse with analog-type RS behavior was demonstrated through photoreduction of GO and N-doped carbon quantum dot (NCQD) nanocomposites. Ultraviolet light irradiation induced the local reduction of GO near the NCQDs, therefore forming multiple weak conductive filaments and demonstrating analog RS with a continuous conductance change. This analog RS enabled the close emulation of several essential synaptic plasticity behaviors; more importantly, the high linearity of the conductance change also facilitated the implementation of pattern recognition with high accuracy. Furthermore, the all-carbon memristive synapse can be transferred onto diverse substrates, showing good flexibility and 3D conformality. Memristive potentiation/depression was stably performed at 450 K, indicating the resistance of the synapse to high temperature. The photoreduction method provides a new path for the fabrication of all-carbon memristive synapses, which supports the development of wearable neuromorphic electronics.
An energy-efficient memristive synapse is highly desired for the development of brain-like neurosynaptic chips. In this work, a ZnO-based memristive synapse with ultralow-power consumption was achieved by simple N-doping. The introduction of N atoms, as the acceptor, reduces the carrier concentration and greatly increases the resistance of the ZnO film. The low energy consumption, which is as low as 60 fJ per synaptic event, can be achieved in our device. Essential synaptic learning functions have been demonstrated, including excitatory postsynaptic current, paired-pulse facilitation, and experience-dependent learning behaviors. Furthermore, the device can still exhibit the synaptic performance in the bent state or even after 100 bending cycles. Our memristive synapse is not only promising for energy-efficient neuromorphic computing systems but also suitable for the development of wearable neuromorphic electronics.
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