Synaptic Plasticity and Metaplasticity of Biological Synapse Realized in a KNbO3 Memristor for Application to Artificial Synapse

Inhibition of Rho-kinase by fasudil contributes to the modulation of the synaptic plasticity response in the rat hippocampus

Metaplasticity refers to an activity-dependent change in the physiological or biochemical state of neurons that changes their ability to generate subsequently induced synaptic plasticity, such as long-term potentiation (LTP) or long-term depression (LTD). Rho-kinases (ROCK) are known to be important for stable changes in synaptic strength, especially LTP. In this study, we investigated whether LTP inhibition in synapses primed with 1-Hz stimulation was affected by ROCK inhibition in young adult male rats. The study also examined the pattern of tau phosphorylation that occurs during metaplastic regulation, applying into perspective the phosphorylation of tau protein by ROCK. Field potentials consisting of an excitatory postsynaptic potential (fEPSP) and population spike (PS) were recorded from the granule cell layer of the hippocampal dentate gyrus (DG). Metaplastic LTP was induced by strong tetanic stimulation (HFS) of the lateral perforant path after a low-frequency stimulation (LFS) protocol. A glass micropipette was inserted into the granule cell layer of the ipsilateral dentate gyrus to record fEPSP and drug infusion. Drug infusion (saline, n = 8; fasudil, n = 8, 10 µM) was started after the 15-min baseline recording and lasted for 60 min. Total and phosphorylated tau levels were measured in the stimulated hippocampus, which was immediately removed after the electrophysiological recording. LFS prevented the induction of LTP in response to HFS and even produced synaptic LTD in the saline-infused group (83.8 ± 2.6% of the baseline), but moderate potentiation of fEPSP (121.1 ± 7.7% of the baseline) occurred at the end of recording in the experiments where fasudil infusion was performed. LFS caused a comparable early depression, and HFS resulted in a comparable potentiation of the PS amplitude in both groups. Granular cells of the DG failed to exhibit synaptic LTP inhibition in the presence of fasudil, and levels of total and phosphorylated GSK-3β and levels of phosphorylated tau (Ser396 and Ser202-Thr205) were found to be lower than those of the control group. Based on these findings, it can be concluded that pharmacological inhibition of ROCK results in impaired ability of dentate gyrus neurons to inhibit synaptic LTP, and this result is accompanied by decreased phosphorylation of GSK-3β and tau proteins. The negative effect of fasudil on neuronal function should not be neglected when evaluating its effects as a therapeutic agent for diseases.

Advances in perovskite-based neuromorphic computing devices

Neuromorphic computing devices, inspired by the architecture and functionality of the human brain, offer a promising solution to the limitations imposed by the von Neumann bottleneck on contemporary computing systems. Perovskite materials are widely used in the photosensitive layer of neuromorphic computing devices due to their high light absorption coefficient and excellent carrier mobility. Here, we summarise the latest research progress on neural morphology computing devices based on perovskite materials with different structures and summarise different application scenarios. Finally, we discussed the issues that still need to be addressed and looked forward to the future development of neural morphology calculations based on perovskite materials.

Scaled-down ionic liquid-functionalized geopolymer memristors

Whereas most memristors are fabricated using sophisticated and expensive manufacturing methods, we recently introduced low-cost memristors constructed from sustainable, porous geopolymers (GP) at room temperature via simple casting processes. These devices exhibit resistive switching via electroosmosis and voltage-driven ion mobility inside water-filled channels within the porous material, enabling promising synaptic properties. However, GP memristors were previously fabricated at the centimeter scale, too large for space-efficient neuromorphic computing applications, and displayed limited memory retention durations due to water evaporation from the pores of the GP material. In this work, we overcome these limitations by implementing (i) an inexpensive manufacturing method that allows fabrication at micron-scale (99.998% smaller in volume than their centimeter-scale counterparts) and (ii) functionalization of GPs with EMIM+ Otf- ionic liquid (IL), which prolonged retention of the memristive switching properties by 50%. This improved class of GP-based memristors also demonstrated ideal synaptic properties in terms of paired-pulse facilitation (PPF), paired-pulse depression (PPD), and spike time dependent plasticity (STDP). These improvements pave the way for using IL-functionalized GP memristors in neuromorphic computing applications, including reservoir computing, in-memory computing, memristors crossbar arrays, and more.

Metaplasticity-Enabled Graphene Quantum Dot Devices for Mitigating Catastrophic Forgetting in Artificial Neural Networks

The limitations of deep neural networks in continuous learning stem from oversimplifying the complexities of biological neural circuits, often neglecting the dynamic balance between memory stability and learning plasticity. In this study, artificial synaptic devices enhanced with graphene quantum dots (GQDs) that exhibit metaplasticity is introduced, a higher-order form of synaptic plasticity that facilitates the dynamic regulation of memory and learning processes similar to those observed in biological systems. The GQDs-assisted devices utilize interface-mediated modifications in asymmetric conductive pathways, replicating classical synaptic plasticity mechanisms. This allows for repeatable and linearly programmable adjustments to future weight changes linked to historical weights. Incorporating metaplasticity is essential for achieving generalization within deep neural networks, which enables them to adapt more fluidly to new information while retaining previously acquired knowledge. The GQDs-device-based system achieved a 97% accuracy on the fourth MNIST dataset task, while consistently achieving performance levels above 94% on prior tasks. This performance substantiates the feasibility of directly transferring metaplasticity principles to deep neural networks, thereby addressing the challenges associated with catastrophic forgetting. These findings present a promising hardware solution for developing neuromorphic systems with robust and sustained learning capabilities that can effectively bridge the gap between artificial and biological neural networks.

Colossal Strain Tuning of Ferroelectric Transitions in KNbO3 Thin Films

Strong coupling between polarization (P) and strain (ɛ) in ferroelectric complex oxides offers unique opportunities to dramatically tune their properties. Here colossal strain tuning of ferroelectricity in epitaxial KNbO3 thin films grown by sub-oxide molecular beam epitaxy is demonstrated. While bulk KNbO3 exhibits three ferroelectric transitions and a Curie temperature (Tc) of ≈676 K, phase-field modeling predicts that a biaxial strain of as little as -0.6% pushes its Tc > 975 K, its decomposition temperature in air, and for -1.4% strain, to Tc > 1325 K, its melting point. Furthermore, a strain of -1.5% can stabilize a single phase throughout the entire temperature range of its stability. A combination of temperature-dependent second harmonic generation measurements, synchrotron-based X-ray reciprocal space mapping, ferroelectric measurements, and transmission electron microscopy reveal a single tetragonal phase from 10 K to 975 K, an enhancement of ≈46% in the tetragonal phase remanent polarization (Pr), and a ≈200% enhancement in its optical second harmonic generation coefficients over bulk values. These properties in a lead-free system, but with properties comparable or superior to lead-based systems, make it an attractive candidate for applications ranging from high-temperature ferroelectric memory to cryogenic temperature quantum computing.

Hydrogel-Gated FETs in Neuromorphic Computing to Mimic Biological Signal: A Review

Hydrogel-gated synaptic transistors offer unique advantages, including biocompatibility, tunable electrical properties, being biodegradable, and having an ability to mimic biological synaptic plasticity. For processing massive data with ultralow power consumption due to high parallelism and human brain-like processing abilities, synaptic transistors have been widely considered for replacing von Neumann architecture-based traditional computers due to the parting of memory and control units. The crucial components mimic the complex biological signal, synaptic, and sensing systems. Hydrogel, as a gate dielectric, is the key factor for ionotropic devices owing to the excellent stability, ultra-high linearity, and extremely low operating voltage of the biodegradable and biocompatible polymers. Moreover, hydrogel exhibits ionotronic functions through a hybrid circuit of mobile ions and mobile electrons that can easily interface between machines and humans. To determine the high-efficiency neuromorphic chips, the development of synaptic devices based on organic field effect transistors (OFETs) with ultra-low power dissipation and very large-scale integration, including bio-friendly devices, is needed. This review highlights the latest advancements in neuromorphic computing by exploring synaptic transistor developments. Here, we focus on hydrogel-based ionic-gated three-terminal (3T) synaptic devices, their essential components, and their working principle, and summarize the essential neurodegenerative applications published recently. In addition, because hydrogel-gated FETs are the crucial members of neuromorphic devices in terms of cutting-edge synaptic progress and performances, the review will also summarize the biodegradable and biocompatible polymers with which such devices can be implemented. It is expected that neuromorphic devices might provide potential solutions for the future generation of interactive sensation, memory, and computation to facilitate the development of multimodal, large-scale, ultralow-power intelligent systems.

Interface Engineering Enables Multilevel Resistive Switching in Ultra-Low-Power Chemobrionic Copper Silicate

Memristor is assuming prominence due to its exceptionally low power consumption, adaptable, and parallel signal processing capabilities that address the limitations of the von Neumann architecture to meet the growing demand for advanced technologies such as artificial intelligence, Internet of Things (IoTs), and neuromorphic computation. In this work, we demonstrate resistive switching in copper silicate-based hollow tube-forming self-organized membrane structures belonging to the category of chemobrionics or chemical gardens to demonstrate cost-effective and highly efficient memristor devices. The device architecture is configured as ITO/PEDOT:PSS/active layer (copper silicate)/PMMA/Ag, an arrangement that serves to stabilize current-voltage hysteresis and exhibit a low SET voltage ∼0.2 V with a 0.8 nJ power consumption while manifesting robust data endurance and multilevel resistive switching. The inherent self-rectifying behavior, characterized by a high rectification ratio of 60, underscores the potential utility of these devices across a spectrum of electronic applications. To emulate the functionality of biological synapses, fundamental synaptic characteristics are assessed, including paired-pulse facilitation (PPF) and potentiation and depression (P&D). We validate the potential of copper silicate chemical garden-based memristor devices for applications that require real-time synaptic processing. Importantly, the fabrication of these devices was accomplished through a comprehensive solution-based, low-temperature process conducted under ambient environmental conditions, obviating the need for specialized glovebox facilities.

Enhanced Multiwavelength Response of Flexible Synaptic Transistors for Human Sunburned Skin Simulation and Neuromorphic Computation

In biological species, optogenetics and bioimaging work together to regulate the function of neurons. Similarly, the light-controlled artificial synaptic system not only enhances computational speed but also simulates complex synaptic functions. However, reported synaptic properties are mainly limited to mimicking simple biological functions and single-wavelength responses. Therefore, the development of flexible synaptic devices with multiwavelength optical signal response and multifunctional simulation remains a challenge. Here, flexible organic light-stimulated synaptic transistors (LSSTs) enabled by alumina oxide (AlOX ), with a simple fabrication process, are reported. By embedding AlOX nanoparticles, the excitons separation efficiency is improved, allowing for multiple wavelength responses. Optimized LSSTs can respond to multiple optical and electrical signals in a highly synaptic manner. Multiwavelength optical synaptic plasticity, electrical synaptic plasticity, sunburned skin simulation, learning efficiency model controlled by photoelectric cooperative stimulation, neural network computing, "deer" picture learning and memory functions are successfully proposed, which promote the development for future artificial intelligent systems. Furthermore, as prepared flexible transistors exhibit mechanical flexibility with bending radius down to 2.5 mm and improved photosynaptic plasticity, which facilitating development of neuromorphic computing and multifunction integration systems at the device-level.

Strain Tuning of Negative Capacitance in Ferroelectric KNbO3 Thin Films

Ferroelectrics with negative capacitance effects can amplify the gate voltage in field-effect transistors to achieve low power operation beyond the limits of Boltzmann's Tyranny. The reduction of power consumption depends on the capacitance matching between the ferroelectric layer and gate dielectrics, which can be well controlled by adjusting the negative capacitance effect in ferroelectrics. However, it is a great challenge to experimentally tune the negative capacitance effect. Here, the observation of the tunable negative capacitance effect in ferroelectric KNbO₃ through strain engineering is demonstrated. The magnitude of the voltage reduction and negative slope in polarization-electric field (P-E) curves as the symbol of negative capacitance effects can be controlled by imposing various epitaxial strains. The adjustment of the negative curvature region in the polarization-energy landscape under different strain states is responsible for the tunable negative capacitance. Our work paves the way for fabricating low-power devices and further reducing energy consumption in electronics.

Perspective on Nanofluidic Memristors: from Mechanism to Application

Nanofluidic memristors are memory resistors based on nanoconfined fluidic systems exhibiting history-dependent ion conductivity. Toward establishing powerful computing systems beyond the Harvard architecture, these ion-based neuromorphic devices attracted enormous research attention owing to the unique characteristics of ion-based conductors. However, the design of nanofluidic memristor is still at a primary state and a systematic guidance on the rational design of nanofluidic system is desperately required for the development of nanofluidic-based neuromorphic devices. Herein, we proposed a systematic review on the history, main mechanism and potential application of nanofluidic memristors in order to give a prospective view on the design principle of memristors based on nanofluidic systems. Furthermore, based on the present status of these devices, some fundamental challenges for this promising area were further discussed to show the possible application of these ion-based devices.

Palimpsest memories stored in memristive synapses

Biological synapses store multiple memories on top of each other in a palimpsest fashion and at different time scales. Palimpsest consolidation is facilitated by the interaction of hidden biochemical processes governing synaptic efficacy during varying lifetimes. This arrangement allows idle memories to be temporarily overwritten without being forgotten, while previously unseen memories are used in the short term. While embedded artificial intelligence can greatly benefit from this functionality, a practical demonstration in hardware is missing. Here, we show how the intrinsic properties of metal-oxide volatile memristors emulate the processes supporting biological palimpsest consolidation. Our memristive synapses exhibit an expanded doubled capacity and protect a consolidated memory while up to hundreds of uncorrelated short-term memories temporarily overwrite it, without requiring specialized instructions. We further demonstrate this technology in the context of visual working memory. This showcases how emerging memory technologies can efficiently expand the capabilities of artificial intelligence hardware toward more generalized learning memories.

'Stateful' threshold switching for neuromorphic learning

Memristors have promising prospects in developing neuromorphic chips that parallel the brain-level power efficiency and brain-like computational functions. However, the limited available ON/OFF states and high switching voltage in conventional resistive switching (RS) constrain its practical and flexible implementations to emulate biological synaptic functions with low power consumption. We present 'stateful' threshold switching (TS) within the millivoltage range depending on the resistive states of RS, which originates from the charging/discharging parasitic elements of a memristive circuit. Fundamental neuromorphic learning can be facilely implemented based on a single memristor by utilizing four resistive states in 'stateful' TS. Besides the metaplasticity of synaptic learning-forgetting behaviors, multifunctional associative learning, involving acquisition, extinction, recovery, generalization and protective inhibition, was realized with nonpolar operation and power consumption of 5.71 pW. The featured 'stateful' TS with flexible tunability, enriched states, and ultralow operating voltage will provide new directions toward a massive storage unit and bio-inspired neuromorphic system.

A Smarter Pavlovian Dog with Optically Modulated Associative Learning in an Organic Ferroelectric Neuromem

Associative learning is a critical learning principle uniting discrete ideas and percepts to improve individuals' adaptability. However, enabling high tunability of the association processes as in biological counterparts and thus integration of multiple signals from the environment, ideally in a single device, is challenging. Here, we fabricate an organic ferroelectric neuromem capable of monadically implementing optically modulated associative learning. This approach couples the photogating effect at the interface with ferroelectric polarization switching, enabling highly tunable optical modulation of charge carriers. Our device acts as a smarter Pavlovian dog exhibiting adjustable associative learning with the training cycles tuned from thirteen to two. In particular, we obtain a large output difference (>103), which is very similar to the all-or-nothing biological sensory/motor neuron spiking with decrementless conduction. As proof-of-concept demonstrations, photoferroelectric coupling-based applications in cryptography and logic gates are achieved in a single device, indicating compatibility with biological and digital data processing.

Effect of Oxygen Vacancy on the Conduction Modulation Linearity and Classification Accuracy of Pr0.7Ca0.3MnO3 Memristor

An amorphous Pr_0.7Ca_0.3MnO₃ (PCMO) film was grown on a TiN/SiO₂/Si (TiN–Si) substrate at 300 °C and at an oxygen pressure (OP) of 100 mTorr. This PCMO memristor showed typical bipolar switching characteristics, which were attributed to the generation and disruption of oxygen vacancy (OV) filaments. Fabrication of the PCMO memristor at a high OP resulted in nonlinear conduction modulation with the application of equivalent pulses. However, the memristor fabricated at a low OP of 100 mTorr exhibited linear conduction modulation. The linearity of this memristor improved because the growth and disruption of the OV filaments were mostly determined by the redox reaction of OV owing to the presence of numerous OVs in this PCMO film. Furthermore, simulation using a convolutional neural network revealed that this PCMO memristor has enhanced classification performance owing to its linear conduction modulation. This memristor also exhibited several biological synaptic characteristics, indicating that an amorphous PCMO thin film fabricated at a low OP would be a suitable candidate for artificial synapses.

Tunable Multilevel Data Storage Bioresistive Random Access Memory Device Based on Egg Albumen and Carbon Nanotubes

In this paper, a tuneable multilevel data storage bioresistive memory device is prepared from a composite of multiwalled carbon nanotubes (MWCNTs) and egg albumen (EA). By changing the concentration of MWCNTs incorporated into the egg albumen film, the switching current ratio of aluminium/egg albumen:multiwalled carbon nanotubes/indium tin oxide (Al/EA:MWCNT/ITO) for resistive random access memory increases as the concentration of MWCNTs decreases. The device can achieve continuous bipolar switching that is repeated 100 times per cell with stable resistance for 104 s and a clear storage window under 2.5 × 104 continuous pulses. Changing the current limit of the device to obtain low-state resistance values of different states achieves multivalue storage. The mechanism of conduction can be explained by the oxygen vacancies and the smaller number of iron atoms that are working together to form and fracture conductive filaments. The device is nonvolatile and stable for use in rewritable memory due to the adjustable switch ratio, adjustable voltage, and nanometre size, and it can be integrated into circuits with different power consumption requirements. Therefore, it has broad application prospects in the fields of data storage and neural networks.

Synaptic metaplasticity in binarized neural networks

While deep neural networks have surpassed human performance in multiple situations, they are prone to catastrophic forgetting: upon training a new task, they rapidly forget previously learned ones. Neuroscience studies, based on idealized tasks, suggest that in the brain, synapses overcome this issue by adjusting their plasticity depending on their past history. However, such "metaplastic" behaviors do not transfer directly to mitigate catastrophic forgetting in deep neural networks. In this work, we interpret the hidden weights used by binarized neural networks, a low-precision version of deep neural networks, as metaplastic variables, and modify their training technique to alleviate forgetting. Building on this idea, we propose and demonstrate experimentally, in situations of multitask and stream learning, a training technique that reduces catastrophic forgetting without needing previously presented data, nor formal boundaries between datasets and with performance approaching more mainstream techniques with task boundaries. We support our approach with a theoretical analysis on a tractable task. This work bridges computational neuroscience and deep learning, and presents significant assets for future embedded and neuromorphic systems, especially when using novel nanodevices featuring physics analogous to metaplasticity.

Solvothermal Reaction and Piezoelectric Response of Oriented KNbO3 Polycrystals

KNbO₃ (KN) piezoelectric polycrystals were prepared by a two-step solvothermal reaction process with the managed organic solvents as reaction mediums at a low temperature for a short time. In the solvothermal reaction system, the formation mechanism of polycrystalline KN is mainly the dissolution-deposition mechanism. The influences of alkalinity, viscosity, and the polarity for reaction mediums on the formation of the niobates were investigated. The chemical reaction mechanisms of niobate products and formation mechanism of niobate crystals from the precursor were clarified. The regulating and controlling mechanism of the phase compositions, the morphologies, and the lattice constants for the niobates obtained in varied reaction mediums were revealed. The obtained KN piezoelectric polycrystals are constructed from oriented KN nanocrystals. Piezoelectric hysteresis loops of cuboid KN polycrystals were detected for the first time. A prepared cuboid KN polycrystal shows an average d₃₃* value of 32 pm/V. The study provides a strategy for the development of oriented KN piezoelectric materials to apply the orientation engineering.

Artificial Tactile Perceptual Neuron with Nociceptive and Pressure Decoding Abilities

The neural system is a multifunctional perceptual learning system. Our brain can perceive different kinds of information to form senses, including touch, sight, hearing, and so on. Mimicking such perceptual learning systems is critical for neuromorphic platform applications. Here, an artificial tactile perceptual neuron is realized by utilizing electronic skins (E-skin) with oxide neuromorphic transistors, and this artificial tactile perceptual neuron successfully simulates biological tactile afferent nerves. First, the E-skin device is constructed using microstructured polydimethylsiloxane membranes coated with Ag/indium tin oxide (ITO) layers, exhibiting good sensitivities of ∼2.1 kPa^-1 and fast response time of tens of milliseconds. Then, the chitosan-based electrolyte-gated ITO neuromorphic transistor is fabricated and exhibits high performance and synaptic responses. Finally, the integrated artificial tactile perceptual neuron demonstrates pressure excitatory postsynaptic current and paired-pulse facilitation. The artificial tactile perceptual neuron is featured with low energy consumption as low as ∼0.7 nJ. Moreover, it can mimic acute and chronic pain and nociceptive characteristics of allodynia and hyperalgesia in biological nociceptors. Interestingly, the artificial tactile perceptual neuron can employ "Morse code" pressure-interpreting scheme. This simple and low-cost approach has excellent potential for applications including but not limited to intelligent humanoid robots and replacement neuroprosthetics.

A dual mode electronic synapse based on layered SnSe films fabricated by pulsed laser deposition

An artificial synapse, such as a memristive electronic synapse, has caught world-wide attention due to its potential in neuromorphic computing, which may tremendously reduce computer volume and energy consumption. The introduction of layered two-dimensional materials has been reported to enhance the performance of the memristive electronic synapse. However, it is still a challenge to fabricate large-area layered two-dimensional films by scalable methods, which has greatly limited the industrial application potential of two-dimensional materials. In this work, a scalable pulsed laser deposition (PLD) method has been utilized to fabricate large-area layered SnSe films, which are used as the functional layers of the memristive electronic synapse with dual modes. Both long-term memristive behaviour with gradually changed resistance (Mode 1) and short-term memristive behavior with abruptly reduced resistance (Mode 2) have been achieved in this SnSe-based memristive electronic synapse. The switching between Mode 1 and Mode 2 can be realized by a series of voltage sweeping and programmed pulses. The formation and recovery of Sn vacancies were believed to induce the short-term memristive behaviour, and the joint action of Ag filament formation/rupture and Schottky barrier modulation can be the origin of long-term memristive behaviour. DFT calculations were performed to further illustrate how Ag atoms and Sn vacancies diffuse through the SnSe layer and form filaments. The successful emulation of synaptic functions by the layered chalcogenide memristor fabricated by the PLD method suggests the application potential in future neuromorphic computers.

Improvement of Conductance Modulation Linearity in a Cu2+-Doped KNbO3 Memristor through the Increase of the Number of Oxygen Vacancies

The Pt/KNbO₃/TiN/Si (KN) memristor exhibits various biological synaptic properties. However, it also displays nonlinear conductance modulation with the application of identical pulses, indicating that it should be improved for neuromorphic applications. The abrupt change of the conductance originates from the inhomogeneous growth/dissolution of oxygen vacancy filaments in the KN film. The change of the filaments in a KN film is controlled by two mechanisms with different growth/dissolution rates: a redox process with a fast rate and an oxygen vacancy diffusion process with a slow rate. Therefore, the conductance modulation linearity can be improved if the growth/dissolution of the filaments is controlled by only one mechanism. When the number of oxygen vacancies in the KN film was increased through doping of Cu²⁺ ions, the growth/dissolution of the filaments in the Cu²⁺-doped KN (CKN) film was mainly influenced by the redox process of oxygen vacancies. Therefore, the CKN film exhibited improved conductance modulation linearity, confirming that the linearity of conductance modulation can be improved by increasing the number of oxygen vacancies in the memristor. This method can be applied to other memristors to improve the linearity of conductance modulation. The CKN memristor also provides excellent biological synaptic characteristics for neuromorphic computing systems.

Improvement of Conductance Modulation Linearity in a Cu2+-Doped KNbO3 Memristor through the Increase of the Number of Oxygen Vacancies

The Pt/KNbO₃/TiN/Si (KN) memristor exhibits various biological synaptic properties. However, it also displays nonlinear conductance modulation with the application of identical pulses, indicating that it should be improved for neuromorphic applications. The abrupt change of the conductance originates from the inhomogeneous growth/dissolution of oxygen vacancy filaments in the KN film. The change of the filaments in a KN film is controlled by two mechanisms with different growth/dissolution rates: a redox process with a fast rate and an oxygen vacancy diffusion process with a slow rate. Therefore, the conductance modulation linearity can be improved if the growth/dissolution of the filaments is controlled by only one mechanism. When the number of oxygen vacancies in the KN film was increased through doping of Cu²⁺ ions, the growth/dissolution of the filaments in the Cu²⁺-doped KN (CKN) film was mainly influenced by the redox process of oxygen vacancies. Therefore, the CKN film exhibited improved conductance modulation linearity, confirming that the linearity of conductance modulation can be improved by increasing the number of oxygen vacancies in the memristor. This method can be applied to other memristors to improve the linearity of conductance modulation. The CKN memristor also provides excellent biological synaptic characteristics for neuromorphic computing systems.

Memristor in a Reservoir System-Experimental Evidence for High-Level Computing and Neuromorphic Behavior of PbI2

Lead halides in an asymmetric layered structure form memristive devices which are controlled by the electronic structure of the PbX₂|metal interface. In this paper, we explain the mechanism that stands behind the I- V pinched hysteresis loop of the device and shortly present its synaptic-like plasticity (spike-timing-dependent plasticity and spike-rate-dependent plasticity) and nonvolatile memory effects. This memristive element was incorporated into a reservoir system, in particular, the echo-state network with delayed feedback, which exhibits brain-like recurrent behavior and demonstrates metaplasticity as one of the available learning mechanisms. It can serve as a classification system that classifies input signals according to their amplitude.

Dendrite Integration Mimicked on Starch-Based Electrolyte-Gated Oxide Dendrite Transistors

Emulation of dendrite integration on brain-inspired hardware devices is of great significance for neuromorphic engineering. Here, solution-processed starch-based electrolyte films are fabricated, demonstrating strong proton gating activities. Starch gated oxide dendrite transistors with multigates are fabricated, exhibiting good electrical performances. Most importantly, dendrite modulation, spatiotemporal dendrite integration, and linear/superlinear dendrite algorithm are demonstrated on the proposed dendrite transistor. Furthermore, a low energy consumption of ∼1.2 pJ is obtained for triggering a synaptic response on the dendrite transistor. Accordingly, the signal-to-noise ratio is still as high as ∼2.9, indicating a high sensitivity of ∼4.6 dB. Such artificial dendrite transistors have potential applications in brain-inspired neuromorphic platforms.