Biological Synapses Research Articles

Nonlinear memristor model with exact solution allows for ex situ reservoir computing training and in situ inference.

Memristive physical reservoir computing is a promising approach for solving data classification and temporal processing tasks. This method exploits the nonlinear dynamics of physical, low-power devices to achieve high-dimensional mapping of input signals. Ion-channel-based memristors, which operate with similar voltages, currents, and timescales as biological synapses, are promising due to their rich dynamics, especially for use in biological edge settings. Accurate modeling of their dynamics is essential for optimizing network hyperparameters ex situ to save time and energy. Here, a generalized sigmoidal growth model of ion-channel memristor conductance is presented and shown to be more accurate in predicting dynamics than linear or logistic models. Using the exact solution of the proposed sigmoidal model, the MNIST handwritten digit classification task is optimized and trained ex situ, then tested in situ with the same trained weights. This approach achieved an experimental testing accuracy of 90.6%.

Dynamic FeOx/FeWOx nanocomposite memristor for neuromorphic and reservoir computing.

Memristors are crucial in computing due to their potential for miniaturization, energy efficiency, and rapid switching, making them particularly suited for advanced applications such as neuromorphic computing and in-memory operations. However, these tasks often require different operational modes-volatile or nonvolatile. This study introduces a forming-free Ag/FeOx/FeWOx/Pt nanocomposite memristor capable of both operational modes, achieved through compliance current (CC) adjustment and structural engineering. Volatile switching occurs at low CC levels (<500 μA), transitioning to nonvolatile at higher levels (mA). Operating at extremely low voltages (<0.2 V), this memristor exhibits excellent uniformity, data retention, and multilevel switching, making it highly suitable for high-density data storage. The memristor successfully mimics fundamental biological synapse functions, exhibiting potentiation, depression, and spike-rate dependent plasticity (SRDP). It effectively emulates transitions from short-term memory (STM) to long-term memory (LTM) by varying pulse characteristics. Leveraging its volatile switching and STM features, the memristor proves ideal for reservoir computing (RC), where it can emulate dynamic reservoirs for sequence data classification. A physical RC system, implemented using digits 0 to 9, achieved a recognition rate of 93.4% in off-chip training with a deep neural network (DNN), confirming the memristor's effectiveness. Overall, the dual-mode switching capability of the Ag/FeOx/FeWOx/Pt memristor enhances its potential for AI applications, particularly in temporal and sequential data processing.

Development of Biodegradable Substrates and Synaptic Transistors for Next‐Generation Transient Electronics

AbstractUbiquitous electronic gadgets in lives have led to an increase in electronic waste (e‐waste), posing a threat to the environment and ecology that must be addressed. This work demonstrates the use of gelatin, a natural protein, for development of flexible biodegradable substrates and synaptic transistors using the same material as gate dielectric. The fabricated p‐channel transistors exhibit high electrical stability and exceptional synaptic characteristics through spike timing dependent plasticity (STDP), spike voltage dependent plasticity (SVDP), and spike number dependent plasticity (SNDP), respectively upon variation of post‐synaptic current (PSC) with time, amplitude, and number of stimuli. These devices exhibit pulse paired facilitation (PPF) with relaxation time constants in the range of ≈10 ms and regulating modulation amplitude of 1 greatly resembling a biological synapse. Study on the variability among distinct devices and over multiple cycles demonstrate outstanding repeatability of synaptic plasticity. The devices showcase significant PSC values with almost linear SNDP, while consuming an ultralow power of ≈11.7 fJ. Excellent stability is observed when subjected to multiple bending sequences. Complete dissolution of these devices in aqueous environments in an hour without any alteration to temperature or pH confirms excellent biodegradability of these devices leading toward transient neuromorphic circuits and systems that adhere to the concepts of circular economy.

Composition-Graded Nitride Ferroelectrics Based Multi-Level Non-Volatile Memory for Neuromorphic Computing.

Multi-level non-volatile ferroelectric memories are emerging as promising candidates for data storage and neuromorphic computing applications, due to the enhancement of storage density and the reduction of energy and space consumption. Traditional multi-level operations are achieved by utilizing intermediary polarization states, which exhibit an unpredictable ferroelectric domain switching nature, leading to unstable multi-level memory. In this study, a unique approach of composition-graded ferroelectric ScAlN to achieve tunable operating voltage in a wide range and attain precise control of domain switching and stable multi-level memory is proposed. This non-volatile memory supports multi-level storage up to 7-bit capacities, and exhibits enhanced performance compared to the uniform composition device, showing one order of magnitude higher ON/OFF ratio, 30% reduced working voltage, and up to 50% enhanced tuning window of operating voltage. Finally, the emulation of long-term plasticity and linear weight update akin to biological synapse with high uniformity and reliability are demonstrated. The proposed composition-grading architecture offers new opportunities for next-generation multi-level ferroelectric memories, paving the way for advanced hybrid integration in multifunctional computing systems.

Multi‐Color Synaptic Luminescence in RE‐Doped Ca2SnO4 (RE = Sm3+, Er3+, and La3+)

AbstractRecently, there has been a surge of interest in neuromorphic computation inspired by the extraordinary characteristics of the human brain, such as low energy consumption, parallelism, adaptivity, cognitive abilities, and learning capabilities. Significant research efforts have focused on exploring optical synaptic behaviors in various functional materials. In this study, the potential of red, green and blue (RGB)‐colored long‐persistent luminescence (LPL) in Sm3+/Er3+/La3+‐doped Ca2SnO4 is investigated for synaptic functionality. The luminescence of the samples is continuously enhanced under serial photoexcitation pulse applications, that is, the potentiation process, which is a key feature demonstrated in biological synapses. In addition, multichannel synaptic functionalities in the full‐color range is successfully demonstrated by integrating individual RGB‐colored Sm3+/Er3+/La3+‐doped Ca2SnO4 into a single quantity. To validate the optical synaptic behavior of the samples in neuromorphic computing applications, a reservoir computing (RC) simulation is performed for space‐time data processing using the unique responses of the samples under 4‐bit excitation pulses. The results demonstrated that the multi‐channel synaptic behaviors in the samples should be more valid for utilization in the RC layer than the single channel of synaptic behavior. We suggest this exploration holds promise for the advancement of synaptic devices employing LPL materials.

Resistive switching and artificial synapses performance of co-evaporated Cs3Cu2I5 films

Perovskite memristors have garnered significant interest for their potential simulating artificial synapses; however, the presence of the toxic lead-based perovskites has hindered advancements in this field. In this work, a nontoxic, thickness-controllable Cs3Cu2I5 perovskite functional layer is synthesized through a dual-source vapor deposition for the Ag/Cs3Cu2I5/ITO memristor. The co-evaporation method shows advantages of various element, controllable atomic ratio and thickness, free impurity, and continuously uniform film. This device demonstrates an operating voltage of 1.2 V, a low power consumption of 0.013 W, a retention time exceeding 104 s, and an endurance of over 400 cycles. The synaptic behavior is emulated using the memristor, focusing on phenomena such as short-term potentiation and depression, paired-pulse facilitation, and spike-time-dependent plasticity. The migration of Na+ and Cl− ions, which occurs between the synaptic cleft and the postsynaptic membrane in biological synapses, is analogously represented by the movement of Ag+ ions between functional layer and the bottom electrode of the memristor. This process is further analyzed using the Hodgkin–Huxley neuron model. The Cs3Cu2I5-based memristor shows considerable promise for applications in storage systems and artificial synapses.

A charge trap–based MoSe2 device emulating bio-realistic synaptic functionalities

Abstract Synaptic devices based on two-dimensional (2D) materials are promising toward the development of high-performance and low-power neuromorphic systems. In this study, we report a single-channel, three-terminal-based nonvolatile and multistate device based on 2D MoSe2. The device operates on the principle of trapping and detrapping of electrons at the MoSe2/SiO2 interface, in response to an applied gate voltage, resulting in a nonvolatile modulation of the threshold voltage. The memory behavior is highly reproducible as verified for around 100 cycles of the gate voltage sweep. The multistate behavior of the MoSe2 device was exploited to demonstrate the characteristics of the biological synapse. The device exhibits various synaptic functions, such as potentiation, depression, spike rate–dependent plasticity, spike magnitude–dependent plasticity, and the ability to transition from short-term to long-term memory. The bio-realistic synaptic behavior of the MoSe2 device underscores its promising potential for neuromorphic hardware.

Enhancement of Synaptic Behavior in Organic Electrochemical Transistors via the Introduction of Layer‐by‐Layer Grown Metal‐Organic Framework

AbstractOrganic electrochemical transistors (OECTs) are promising for neuromorphic architectures as they can generate multiple electrical states through the control of ion transport. However, conventional OECTs face limitations in mimicking a fully functional biological synapse due to their inability to achieve long‐term plasticity. In this study, a metal‐organic framework (MOF)‐enhanced OECT (MOECT) is fabricated by introducing MOF into the ion‐organic semiconductor (OSC) layer. MOFs are synthesized using the layer‐by‐layer (LBL) method, and additional cross‐linked OSC is introduced to prevent damage to the semiconductor layers during synthesis. The synthesized MOF layers hinder the rediffusion of ions in OECT, allowing ions to remain in the OSC for an extended period. In this study, the MOECT showed a change in current depending on the doping level, recording a current state 4.4 × 107 times higher than that of pristine OECT. Ultimately, the developed MOECTs are applied as synaptic transistors. MOECTs show 14% higher excitatory postsynaptic current (EPSC) after 130 s compared to pristine OECT, thereby strengthening the long‐term plasticity characteristics of neuromorphic devices. This method enhances the performance of synaptic transistors by introducing MOF, offering various possibilities through the selection of different MOF structures and ions, indicating it is a methodological approach with high potential.

Proton‐Modulated Resistive Switching in a Synapse‐Like Tyrosine‐Rich Peptide‐Based Memristor

AbstractArtificial intelligence has become an essential part of the daily lives and has revolutionized various sectors, including healthcare, finance, transportation, and entertainment. With a substantial increase in processed data, neuromorphic devices that replicate the operation of the human brain have been emphasized owing to their superior efficiency. Typical neuromorphic devices focus on constructing synapse‐like structures. However, biological synapses have more complex mechanisms for efficient data processing. One of the most prominent mechanisms is proton activation, which forms an ion concentration gradient prior to the transmission of neurotransmitters and plays a key role in efficient computation. In this study, proton‐mediated signaling at biological synapses is successfully replicated by fabricating a proton‐modulated memristor device using a tyrosine‐rich peptide film. The ionic input of the memristor is controlled by applying a voltage to proton‐permeable PdHx contacts in a hydrogen atmosphere, thus successfully adjusting the resistive switching behavior. Remarkable improvements in resistive switching and computing performance are observed through proton injection, analogous to “proton‐mediated signaling” at the actual synapse. It is believed that this study proposes a new paradigm for designing biorealistic devices and provides inspiration for precisely controllable ion‐based neuromorphic devices.

Bio‐Realistic Synaptic‐Replicated “V” Type Oxygen Vacancy Memristor

AbstractThe development of an artificial synaptic device is essential for the construction of a brain‐like neuromorphic computing architecture. In this study, the goal is to create a multi‐layer HfOx memristor with a “V” type oxygen vacancy (Vo) distribution to mimic the function of calcium ions (Ca2+) during synaptic information transmission. By adjusting voltage and compliance current (Icc), the HfOx memristor can open or close different levels of volt‐controlled Ca2+ channels, thus enabling the replication of synaptic structure, neurotransmitter release/acceptor, and information transmission. A mathematical model is adopted to describe the behavior of volt‐controlled Ca2+ channels, which demonstrates that the device exhibits consistent characteristics with biological synapses. The device forms an “hourglass” conductive filament (CF) within its middle functional layer, allowing for precise control over filament formation and positioning. This results in ultra‐low power consumption for erasing (581 fJ), fast erasing speed (10 ns), and a low resistance difference coefficient of only 1.8%. Furthermore, the device successfully simulates the physical dynamics of Ca2+ during short‐term potentiation (STP) and long‐term potentiation (LTP) in biological synapses while replicating various guided synaptic behaviors. This study provides a straightforward method for memristors to realize bio‐realistic artificial neuromorphic applications.

Synaptic Properties of an Interfacial Memristor Based on a Ga2O3/Nb:SrTiO3 Heterojunction.

Memristors have been extensively studied for tremendous potential for future neuromorphic computing hardware applications because of their ability to imitate biological synaptic processes. Herein, we report an interfacial memristor based on a Ga2O3/Nb:SrTiO3 heterojunction that shows stable bipolar resistive switching behavior, long retention time, and high switching ratio. The conductance of the Au/Ga2O3/Nb:SrTiO3/In memristor can be gradually modulated under the voltage sweep mode as well as positive and negative pulse voltage stimulations, respectively, thus realizing the long-term potentiation/depression characteristics of the simulated biological synapse. A neural network based on the prepared memristor was built to recognize the handwritten picture data set with a recognition accuracy of 92.78% by using the NeuroSimV3.0 platform. Our work indicates that the Ga2O3/Nb:SrTiO3 heterojunction memristor has significant potential in a neuromorphic computing system.

Artificial synapses based on HfOx/TiOy memristor devices for neuromorphic applications

As a result of enormous progress in nanoscale electronics, interest in artificial intelligence (AI) supported systems has also increased greatly. These systems are typically designed to process computationally intensive data. Parallel processing neural network architectures are particularly noteworthy for their ability to process dense data at high speeds, making them suitable candidates for AI algorithms. Due to their ability to combine processing and memory functions in a single device, memristors offer a significant advantage over other electronic platforms in terms of area scaling efficiency and energy savings. In this study, single-layer and bilayer metal-oxide HfOxand TiOymemristor devices inspired by biological synapses were fabricated by pulsed laser and magnetron sputtering deposition techniques in high vacuum with different oxide thicknesses. The structural and electrical properties of the fabricated devices were analysed using x-ray reflectivity, x-ray photoelectron spectroscopy, and standard two-probe electrical characterization measurements. The stoichiometry and degree of oxidation of the elements in the oxide material for each thin film were determined. Moreover, the switching characteristics of the metal oxide upper layer in bilayer devices indicated its potential as a selective layer for synapse. The devices successfully maintained the previous conductivity values, and the conductivity increased after each pulse and reached its maximum value. Furthermore, the study successfully observed synaptic behaviours with long-term potentiation, long-term depression (LTD), paired-pulse facilitation, and spike-timing-dependent plasticity, showcasing potential of the devices for neuromorphic computing applications.

Ionic Potential Relaxation Effect in a Hydrogel Enabling Synapse-Like Information Processing.

The next-generation brain-like intelligence based on neuromorphic architectures emphasizes learning the ionic language of the brain, aiming for efficient brain-like computation and seamless human-computer interaction. Ionic neuromorphic devices, with ions serving as information carriers, provide possibilities to achieve this goal. Soft and biocompatible ionic conductive hydrogels are an ideal substrate for constructing ionic neuromorphic devices, but it remains a challenge to modulate the ion transport behavior in hydrogels to mimic neuroelectric signals. Here, we describe an ionic potential relaxation effect in a hydrogel device prepared by sandwiching a layer of polycationic hydrogel (CH) between two layers of neutral hydrogel (NH), allowing this device to simulate various electrical signal patterns observed in biological synapses, including short- and long-term plasticity patterns. Theoretical and experimental results show that the selective permeation and hysteretic diffusion of ions caused by the anion selectivity of the CH layer are responsible for potential relaxation. Such an effect allows us with hydrogels to enable synapse-like information processing functions, including tactile perception, learning, memory, and neuromorphic computing. Additionally, the hydrogel device can operate stably even under 180° bending and 50% tensile strain, expanding the pathway for implementing advanced brain-like intelligent systems.

Synaptic Feature of Quantum Dot Light-Emitting Diodes for Visualization of Learning Process.

Brain-inspired electronics with synaptic functions hold significant promise for advancing artificial intelligent applications. In this study, we demonstrate the synaptic feature of quantum-dot light-emitting diodes (QLEDs), which can convert electrical pulses into synapse-like light signals (the brightness gradually increases as the electrical pulses are prolonged). These features are analogous to learning and forgetting in biological synapses. The enhancement of brightness can be attributed to the reduction of charge transfer from the quantum dots to ZnO electron transport layer and resistive switching effect. With an integrated complementary metal-oxide-semiconductor (CMOS) drive, arrayed synaptic QLEDs can simulate the visualization of brain-like learning processes, which can reduce the noise toward high image recognition rate (>95.0%) by deep neural networks. Our findings introduce a novel brain-inspired optoelectronic approach with potential applications in optical neuromorphic systems.

Heterogating Gel Iontronics: A Revolution in Biointerfaces and Ion Signal Transmission.

Currently, existing iontronic systems are limited and struggle to process electronic-to-multi-ionic transport, resulting in interchange inefficiencies and incompatibilities between artificial ion devices and biological tissue interfaces. The development of heterogating gel iontronics offers a significant advancement in bridging this gap, drawing inspiration from the complex ionic transmission mechanisms found in biological synapses within neural networks. These heterogating gels utilize a biphasic architecture, where the heterointerface effect constructs ionic transfer energy barriers, enabling distinct signal transmission among different ions. In systems with multiple ion species, heterogating gel iontronics allow for precise control of ion transmission, realizing hierarchical and selective cross-stage signal transmission as a neuromorphic function. This perspective highlights the vast potential of heterogating iontronics in applications such as biosensing, neuroprosthetics, and ion separation technologies. Meanwhile, it also addresses the current challenges, including scaling production, ensuring biocompatibility, and integrating with existing technologies, which are crucial for future development. The advancement of heterogating gels is expected to promote the integration between abiotic and biotic systems, with broad implications for smart sensors, bioneural devices, and beyond.

Short‐Term and Long‐Term Memory Functionality of a Brain‐Like Device Built from Nanoparticle Atomic Switch Networks

AbstractThe synaptic plasticity of the Ag‐Ag2S nanoparticle‐based volatile memristor system is demonstrated. The nanoparticles self‐assemble into a network with over 103 interconnected atomic switch interfaces. Short‐term plasticity is identified by spontaneous conductance relaxation, attributed to the memristor's volatility. The conductance of the network is enhanced when a subsequent stimulus pulse arrives shortly after the previous one, analogous to the paired‐pulse facilitation in biological synapses. Furthermore, repeated pulse stimulation is used to achieve the transition from short‐term plasticity to long‐term potentiation, a process related to learning and memory formation. Remarkably, the result reveals that the lifetime of long‐term potentiation for 100‐pulse stimulation is 40 min, indicating that the device can forget newly acquired information after prolonged storage, akin to human memories. The findings provide insight into the the learning and memory abilities of atomic switch network memristors, facilitating the development of hardware‐implemented artificial neural networks.

Optoelectronic synapses with chemical-electric behaviors in gallium nitride semiconductors for biorealistic neuromorphic functionality

Optoelectronic synapses, leveraging the integration of classic photo-electric effect with synaptic plasticity, are emerging as building blocks for artificial vision and photonic neuromorphic computing. However, the fundamental working principles of most optoelectronic synapses mainly rely on physical behaviors while missing chemical-electric synaptic processes critical for mimicking biorealistic neuromorphic functionality. Herein, we report a photoelectrochemical synaptic device based on p-AlGaN/n-GaN semiconductor nanowires to incorporate chemical-electric synaptic behaviors into optoelectronic synapses, demonstrating unparalleled dual-modal plasticity and chemically-regulated neuromorphic functions through the interplay of internal photo-electric and external electrolyte-mediated chemical-electric processes. Electrical modulation by implementing closed or open-circuit enables switching of optoelectronic synaptic operation between short-term and long-term plasticity. Furthermore, inspired by transmembrane receptors that connect extracellular and intracellular events, synaptic responses can also be effectively amplified by applying chemical modifications to nanowire surfaces, which tune external and internal charge behaviors. Notably, under varied external electrolyte environments (ion/molecule species and concentrations), our device successfully mimics chemically-regulated synaptic activities and emulates intricate oxidative stress-induced biological phenomena. Essentially, we demonstrate that through the nanowire photoelectrochemical synapse configuration, optoelectronic synapses can be incorporated with chemical-electric behaviors to bridge the gap between classic optoelectronic synapses and biological synapses, providing a promising platform for multifunctional neuromorphic applications.

All-in-One 2D Molecular Crystal Optoelectronic Synapse for Polarization-Sensitive Neuromorphic Visual System.

Neuromorphic visual systems (NVSs) hold the potential to not only preserve but also enhance human visual capabilities. One such augmentation lies in harnessing polarization information from light reflected or scattered off surfaces like bees, which can disclose unique characteristics imperceptible to the human eyes. While creating polarization-sensitive optoelectronic synapses presents an intriguing avenue for equipping NVS with this capability, integrating functions like polarization sensitivity, photodetection, and synaptic operations into a singular device has proven challenging. This integration typically necessitates distinct functional components for each performance metric, leading to intricate fabrication processes and constraining overall performance. Herein, a pioneering linear polarized light sensitive synaptic organic phototransistor (OPT) based on 2D molecular crystals (2DMCs) with highly integrated, all-in-one functionality, is demonstrated. By leveraging the superior crystallinity and molecular thinness of 2DMC, the synaptic OPT exhibits comprehensive superior performance, including a linear dichroic ratio up to 3.85, a high responsivity of 1.47 × 104 A W-1, and the adept emulation of biological synapse functions. A sophisticated application in noncontact fingerprint detection achieves a 99.8% recognition accuracy, further highlights its potential. The all-in-one 2DMC optoelectronic synapse for polarization-sensitive NVS marks a new era for intelligent perception systems.

Optical memcapacitor based on MoS2 quantum capacitance

It is imperative to study neuromorphic devices where the behavior of artificial synapses is regulated by external conditions. The quantum capacitance of 2D materials is determined by the local density of states near the Fermi level and usually exhibits a sensitive response to external stimuli. This study explores the use of MoS2 quantum capacitance in a memcapacitor (MoS2 quantum memcapacitor, MQM), emulating biological synapse behavior and showcasing higher sensitivity. Notably, without altering the physical structure, MQM realizes the potentiation and depression of capacitance through light and gas coupling manipulation, establishing a memory effect for the device. This enables MQM to reproduce the learning-forgetting cycle multiple times with good repeatability. Moreover, MQM can simulate various biological synaptic behaviors, encompassing short-term memory to long-term memory conversions, short-term depression to long-term depression conversions, and higher pair-pulse-facilitation behavior. These characteristics offer a fresh direction and possibilities for artificial synaptic device research.

Enhanced Synaptic Characteristics Under Applied Magnetic Field in V2O5/NiMnIn Based Switching Device for Neuromorphic Computing

The present study reports a memory structure Al/V2O5/NiMnIn on a flexible stainless-steel (SS) substrate for neuromorphic applications. The fabricated device exhibits gradual SET and RESET switching characteristics with an OFF/ON resistance ratio of ~100, good consistency of 4500, and excellent data retention capability up to 3000 s. The current-voltage (I-V) study supports an Ohmic conduction mechanism in the low resistance state (LRS). In contrast, the trap-controlled modified space charge conduction mechanism demonstrated the high resistance state (HRS). The resistance versus temperature measurement (R-T) in the LRS and HRS of the device signifies that oxygen vacancies form the conduction filament. We further analyze the synaptic functioning by applying identical consecutive voltage pulses, and the device's conductance change has been observed. These characteristics show a good representation of the biological memory synapse in terms of the artificial memory device. Long-term potentiation (LTP) and long-term depression (LTD) show nonlinear and asymmetery behavior, which is substantial for neuromorphic applications. A considerable shift in LTP and LTD was detected by applying external temperature and magnetic field. This is explained via temperature and magnetic field strain in the functional NiMnIn bottom electrode of the fabricated device. The mechanical flexibility of the memory structure was tested by exploring the switching characteristics with various bending angles and bending cycles. Therefore, the present study offers new avenues for flexible devices with high data storage capability for futuristic neuromorphic applications.