Memristive Ion Channel-Doped Biomembranes as Synaptic Mimics

Memristive Characteristics in an Asymmetrically Charged Nanochannel

The emergent nanofluidic memristor provides a promising way of emulating neuromorphic functions in the brain. The conical-shaped nanopore showed promising features for a nanofluidic memristor, inspiring us to investigate the memory effects in asymmetrically charged nanochannels due to their high current rectification, which may result in good memory effects. Here, the memory effects of an asymmetrically charged nanofluidic channel were numerically simulated by Poisson-Nernst-Planck equations. Our results showed that the I-V curves represented a diode in low scanning frequency and then became a memristor and finally a resistor as frequency increased. We successfully replicated the learning behavior in our system with history-dependent ion redistribution in the nanochannel. Some critical factors were quantitatively analyzed for the memory effects including voltage amplitude, optimal frequency, and Dukhin number. Experimental characterizations were also carried out. Our findings are useful for the design of nanofluidic memristors by the principle of enrichment and depletion as well as the determination of the best memory settings.

Artificial Funnel Nanochannel Device Emulates Synaptic Behavior

Creating artificial synapses that can interact with biological neural systems is critical for developing advanced intelligent systems. However, there are still many difficulties, including device morphology and fluid selection. Based on Micro-Electro-Mechanical System technologies, we utilized two immiscible electrolytes to form a liquid/liquid interface at the tip of a funnel nanochannel, effectively enabling a wafer-level fabrication, interactions between multiple information carriers, and electron-to-chemical signal transitions. The distinctive ionic transport properties successfully achieved a hysteresis in ionic transport, resulting in adjustable multistage conductance gradient and synaptic functions. Notably, the device is similar to biological systems in terms of structure and signal carriers, especially for the low operating voltage (200 mV), which matches the biological neural potential (∼110 mV). This work lays the foundation for realizing the function of iontronics neuromorphic computing at ultralow operating voltages and in-memory computing, which can break the limits of information barriers for brain-machine interfaces.

Memristive arrangements of nanofluidic pores

We demonstrate that nanofluidic diodes in multipore membranes show a memristive behavior that can be controlled not only by the amplitude and frequency of the external signal but also by series and parallel arrangements of the membranes. Each memristor consists of a polymeric membrane with conical nanopores that allow current rectification due to the electrical interaction between the ionic solution and the pore surface charges. This surface charge-regulated ionic transport shows a rich nonlinear physics, including memory and inductive effects, which are characterized here by the current-voltage curves and electrical impedance spectroscopy. Also, neuromorphiclike potentiation of the membrane conductance following voltage pulses (spikes) is observed. The multipore membrane with nanofluidic diodes shows physical concepts that should have application for information processing and signal conversion in iontronics hybrid devices.

Vertically Stackable Ovonic Threshold Switch Oscillator Using Atomic Layer Deposited Ge0.6Se0.4 Film for High-Density Artificial Neural Networks

Nanodevice oscillators (nano-oscillators) have received considerable attention to implement in neuromorphic computing as hardware because they can significantly improve the device integration density and energy efficiency compared to complementary metal oxide semiconductor circuit-based oscillators. This work demonstrates vertically stackable nano-oscillators using an ovonic threshold switch (OTS) for high-density neuromorphic hardware. A vertically stackable Ge0.6Se0.4 OTS-oscillator (VOTS-OSC) is fabricated with a vertical crossbar array structure by growing Ge0.6Se0.4 film conformally on a contact hole structure using atomic layer deposition. The VOTS-OSC can be vertically integrated onto peripheral circuits without causing thermal damage because the fabrication temperature is <400 °C. The fabricated device exhibits oscillation characteristics, which can serve as leaky integrate-and-fire neurons in spiking neural networks (SNNs) and coupled oscillators in oscillatory neural networks (ONNs). For practical applications, pattern recognition and vertex coloring are demonstrated with SNNs and ONNs, respectively, using semiempirical simulations. This structure increases the oscillator integration density significantly, enabling complex tasks with a large number of oscillators. Moreover, it can enhance the computational speed of neural networks due to its rapid switching speed.

A Diffusive Artificial Synapse Based on Charged Metal Nanoparticles

We show that a diffusive memristor with analogue switching characteristics can be achieved in a layer of gold nanoparticles (AuNPs) functionalized with charged self-assembled monolayers (deprotonated 11-mercaptoundecanoic acid). The nanoparticle core and the anchored stationary charges are jammed within the layer while the mobile counterions [N(CH3)4+] can respond to the electric field and spontaneously diffuse back to the initial positions upon removal of the field. This metal nanoparticle device is set-step free, energy consumption efficient, mechanically flexible, and analogous to bio-Ca2+ dynamics and has tunable conductance modulation capabilities at the counterion concentrations. The gradual resistive switching behavior enables us to implement several important synaptic functions such as potentiation/depression, spike voltage-dependent plasticity, spike duration-dependent plasticity, spike frequency-dependent plasticity, and paired-pulse facilitation. Finally, on the basis of the paired-pulse facilitation characteristics, the metal nanoparticle diffusive artificial synapse is used for edge extraction with exhibits excellent performance.

Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity

Recent advancements in reservoir computing (RC) research have created a demand for analogue devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic memristors, with nonlinear and short-term memory dynamics, are excellent candidates as information-processing devices or reservoirs for temporal classification and prediction tasks. Previous implementations relied on nominally identical memristors that applied the same nonlinear transformation to the input data, which is not enough to achieve a rich state space. To address this limitation, researchers either diversified the data encoding across multiple memristors or harnessed the stochastic device-to-device variability among the memristors. However, this approach requires additional preprocessing steps and leads to synchronization issues. Instead, it is preferable to encode the data once and pass them through a reservoir layer consisting of memristors with distinct dynamics. Here, we demonstrate that ion-channel-based memristors with voltage-dependent dynamics can be controllably and predictively tuned through the voltage or adjustment of the ion channel concentration to exhibit diverse dynamic properties. We show, through experiments and simulations, that reservoir layers constructed with a small number of distinct memristors exhibit significantly higher predictive and classification accuracies with a single data encoding. We found that for a second-order nonlinear dynamical system prediction task, the varied memristor reservoir experimentally achieved an impressive normalized mean square error of 1.5 × 10-3, using only five distinct memristors. Moreover, in a neural activity classification task, a reservoir of just three distinct memristors experimentally attained an accuracy of 96.5%. This work lays the foundation for next-generation physical RC systems that can exploit the complex dynamics of their diverse building blocks to achieve increased signal processing capabilities.

Neuromorphic responses of nanofluidic memristors in symmetric and asymmetric ionic solutions

We show that ionic conduction properties of a multipore nanofluidic memristor can be controlled not only by the amplitude and frequency of an external driving signal but also by chemical gating based on the electrolyte concentration, presence of divalent and trivalent cations, and multi-ionic systems in single and mixed electrolytes. In addition, we describe the modulation of current rectification and hysteresis phenomena, together with neuromorphic conductance responses to voltage pulses, in symmetric and asymmetric external solutions. In our case, memristor conical pores act as nanofluidic diodes modulated by ionic solution characteristics due to the surface charge-regulated ionic transport. The above facts suggest potential sensing and actuating applications based on the conversion between ionic and electronic signals in bioelectrochemical hybrid circuits.

Hydrophobically gated memristive nanopores for neuromorphic applications

Signal transmission in the brain relies on voltage-gated ion channels, which exhibit the electrical behaviour of memristors, resistors with memory. State-of-the-art technologies currently employ semiconductor-based neuromorphic approaches, which have already demonstrated their efficacy in machine learning systems. However, these approaches still cannot match performance achieved by biological neurons in terms of energy efficiency and size. In this study, we utilise molecular dynamics simulations, continuum models, and electrophysiological experiments to propose and realise a bioinspired hydrophobically gated memristive nanopore. Our findings indicate that hydrophobic gating enables memory through an electrowetting mechanism, and we establish simple design rules accordingly. Through the engineering of a biological nanopore, we successfully replicate the characteristic hysteresis cycles of a memristor and construct a synaptic device capable of learning and forgetting. This advancement offers a promising pathway for the realization of nanoscale, cost- and energy-effective, and adaptable bioinspired memristors.

Synaptical Tunability of Multipore Nanofluidic Memristors

We demonstrate a multipore nanofluidic memristor with conical pores showcasing a wide range of hysteresis and memristor properties that provide functionalities for brainlike computation in neuromorphic applications. Leveraging the interplay between the charged functional groups on the pore surfaces and the confined ionic solution, the memristor characteristics are modulated through the electrolyte type, ionic concentrations, and pH levels of the aqueous solution. The multipore membrane mimics the functional characteristics of biological ion channels and displays synaptical potentiation and depression. Furthermore, this property can be inverted in polarity by chemically varying the pH level. The ability to modulate memory effects by ionic conductivity holds promise for enhancing signal information processing capabilities.

Cascade-heterogated biphasic gel iontronics for electronic-to-multi-ionic signal transmission

Currently, electronics and iontronics in abiotic-biotic systems can only use electrons and single-species ions as unitary signal carriers. Thus, a mechanism of gating transmission for multiple biosignals in such devices is needed to match and modulate complex aqueous-phase biological systems. Here we report the use of cascade-heterogated biphasic gel iontronics to achieve diverse electronic-to-multi-ionic signal transmission. The cascade-heterogated property determined the transfer free energy barriers experienced by ions and ionic hydration-dehydration states under an electric potential field, fundamentally enhancing the distinction of cross-interface transmission between different ions by several orders of magnitude. Such heterogated or chemical-heterogated iontronics with programmable features can be coupled with multi-ion cross-interface mobilities for hierarchical and selective cross-stage signal transmission. We expect that such iontronics would be ideal candidates for a variety of biotechnology applications.

Cations Control Lipid Bilayer Memcapacitance Associated with Long-Term Potentiation

Phospholipid bilayers can be described as capacitors whose capacitance per unit area (specific capacitance, Cm) is determined by their thickness and dielectric constant─independent of applied voltage. It is also widely assumed that the Cm of membranes can be treated as a "biological constant". Recently, using droplet interface bilayers (DIBs), it was shown that zwitterionic phosphatidylcholine (PC) lipid bilayers can act as voltage-dependent, nonlinear memory capacitors, or memcapacitors. When exposed to an electrical "training" stimulation protocol, capacitive energy storage in lipid membranes was enhanced in the form of long-term potentiation (LTP), which enables biological learning and long-term memory. LTP was the result of membrane restructuring and the progressive asymmetric distribution of ions across the lipid bilayer during training, which is analogous, for example, to exponential capacitive energy harvesting from self-powered nanogenerators. Here, we describe how LTP could be produced from a membrane that is continuously pumped into a nonequilibrium steady state, altering its dielectric properties. During this time, the membrane undergoes static and dynamic changes that are fed back to the system's potential energy, ultimately resulting in a membrane whose modified molecular structure supports long-term memory storage and LTP. We also show that LTP is very sensitive to different salts (KCl, NaCl, LiCl, and TmCl3), with LiCl and TmCl3 having the most profound effect in depressing LTP, relative to KCl. This effect is related to how the different cations interact with the bilayer zwitterionic PC lipid headgroups primarily through electric-field-induced changes to the statistically averaged orientations of water dipoles at the bilayer headgroup interface.

Modulation of Voltage-Gating and Hysteresis of Lysenin Channels by Cu2+ Ions

The intricate voltage regulation presented by lysenin channels reconstituted in artificial lipid membranes leads to a strong hysteresis in conductance, bistability, and memory. Prior investigations on lysenin channels indicate that the hysteresis is modulated by multivalent cations which are also capable of eliciting single-step conformational changes and transitions to stable closed or sub-conducting states. However, the influence on voltage regulation of Cu2+ ions, capable of completely closing the lysenin channels in a two-step process, was not sufficiently addressed. In this respect, we employed electrophysiology approaches to investigate the response of lysenin channels to variable voltage stimuli in the presence of small concentrations of Cu2+ ions. Our experimental results showed that the hysteretic behavior, recorded in response to variable voltage ramps, is accentuated in the presence of Cu2+ ions. Using simultaneous AC/DC stimulation, we were able to determine that Cu2+ prevents the reopening of channels previously closed by depolarizing potentials and the channels remain in the closed state even in the absence of a transmembrane voltage. In addition, we showed that Cu2+ addition reinstates the voltage gating and hysteretic behavior of lysenin channels reconstituted in neutral lipid membranes in which lysenin channels lose their voltage-regulating properties. In the presence of Cu2+ ions, lysenin not only regained the voltage gating but also behaved like a long-term molecular memory controlled by electrical potentials.

Extended Higher-Order Elements with Frequency-Doubled Parameters: The Hysteresis Loops Are Always of Type II

Current MEMS (Micro Electro Mechanical Systems) can be modeled by state-dependent elements that exhibit hysteretic behavior. Examples include capacitors and inductors whose capacitances and inductances are dependent on the instantaneous state of the electromechanical system, resistors whose resistances exhibit temperature changes when the elements are actually heated, etc. Regardless of the physical background, such hysteresis manifestations can be studied uniformly in the broader framework of generic and extended higher-order elements, in which a classification of hysteretic loops into types I and II is established. The loop type is an important dynamical parameter of an element, having the potential to indicate, for example, its (in)volatility. Thus far, there is no reliable criterion to determine the type of steady loop from the defining relations of an element. This work reports on one special class of extended elements that produces type II loops under all circumstances. The paper presents hitherto unpublished connections between the frequency-doubling parameters of an element and the type of its hysteresis loop. The new findings are expressed by several theorems that allow the type of hysteresis to be inferred from the frequency behavior of the element parameter or state, and vice versa. These procedures are demonstrated with examples and verified by computer simulations.

Organic Memristor-Based Flexible Neural Networks with Bio-Realistic Synaptic Plasticity for Complex Combinatorial Optimization

Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Several studies have been conducted on flexible neural networks for practical applications; however, developing systems with complete synaptic plasticity for combinatorial optimization remains challenging. In this study, the metal-ion injection density is explored as a diffusive parameter of the conductive filament in organic memristors. Additionally, a flexible artificial synapse with bio-realistic synaptic plasticity is developed using organic memristors that have systematically engineered metal-ion injections, for the first time. In the proposed artificial synapse, short-term plasticity (STP), long-term plasticity, and homeostatic plasticity are independently achieved and are analogous to their biological counterparts. The time windows of the STP and homeostatic plasticity are controlled by the ion-injection density and electric-signal conditions, respectively. Moreover, stable capabilities for complex combinatorial optimization in the developed synapse arrays are demonstrated under spike-dependent operations. This effective concept for realizing flexible neuromorphic systems for complex combinatorial optimization is an essential building block for achieving a new paradigm of wearable smart electronics associated with artificial intelligent systems.

Morphogenesis-inspired two-dimensional electrowetting in droplet networks

Living tissues dynamically reshape their internal cellular structures through carefully regulated cell-to-cell interactions during morphogenesis. These cellular rearrangement events, such as cell sorting and mutual tissue spreading, have been explained using the differential adhesion hypothesis, which describes the sorting of cells through their adhesive interactions with their neighbors. In this manuscript we explore a simplified form of differential adhesion within a bioinspired lipid-stabilized emulsion approximating cellular tissues. The artificial cellular tissues are created as a collection of aqueous droplets adhered together in a network of lipid membranes. Since this abstraction of the tissue does not retain the ability to locally vary the adhesion of the interfaces through biological mechanisms, instead we employ electrowetting with offsets generated by spatial variations in lipid compositions to capture a simple form of bioelectric control over the tissue characteristics. This is accomplished by first conducting experiments on electrowetting in droplet networks, next creating a model for describing electrowetting in collections of adhered droplets, then validating the model against the experimental measurements. This work demonstrates how the distribution of voltage within a droplet network may be tuned through lipid composition then used to shape directional contraction of the adhered structure using two-dimensional electrowetting events. Predictions from this model were used to explore the governing mechanics for complex electrowetting events in networks, including directional contraction and the formation of new interfaces.

Learning from the Brain: Bioinspired Nanofluidics

The human brain completes intelligent behaviors such as the generation, transmission, and storage of neural signals by regulating the ionic conductivity of ion channels in neuron cells, which provides new inspiration for the development of ion-based brain-like intelligence. Against the backdrop of the gradual maturity of neuroscience, computer science, and micronano materials science, bioinspired nanofluidic iontronics, as an emerging interdisciplinary subject that focuses on the regulation of ionic conductivity of nanofluidic systems to realize brain-like functionalities, has attracted the attention of many researchers. This Perspective provides brief background information and the state-of-the-art progress of nanofluidic intelligent systems. Two main categories are included: nanofluidic transistors and nanofluidic memristors. The prospects of nanofluidic iontronics' interdisciplinary progress in future artificial intelligence fields such as neuromorphic computing or brain-computer interfaces are discussed. This Perspective aims to give readers a clear understanding of the concepts and prospects of this emerging interdisciplinary field.

Recent progress in bio-voltage memristors working with ultralow voltage of biological amplitude

Neuromorphic systems built from memristors that emulate bioelectrical information processing in the brain may overcome the limitations of traditional computing architectures. However, functional emulation alone may still not attain all the merits of bio-computation, which uses action potentials of 50-120 mV at least 10 times lower than signal amplitude in conventional electronics to achieve extraordinary power efficiency and effective functional integration. Reducing the functional voltage in memristors to this biological amplitude can thus advance neuromorphic engineering and bio-emulated integration. This review aims to provide a timely update on the effort and progress in this burgeoning research direction, covering the aspects of device material composition, performance, working mechanism, and potential application.

Threshold switching in nickel-doped zinc oxide based memristor for artificial sensory applications

Electronic devices featuring biomimetic behaviour as electronic synapses and neurons have motivated the emergence of a new era in information and humanoid robotics technologies. In the human body, a nociceptor is a unique sensory neuron receptor that is capable of detecting harmful signals, leading to the central nervous system initiating a motor response. Herein, a nickel-doped zinc oxide (NZO)/Au based memristor is fabricated for the first time and characterized for artificial nociceptor application. For this, the introduction of a nickel-doped zinc oxide (NZO) layer between P⁺⁺-Si and Au electrodes is used to eliminate the surface effects of the NZO layer, resulting in improved volatile threshold switching performance. Depending on the intensity, duration, and repetition rate of the external stimuli, this newly created memristor exhibits various critical nociceptive functions, including threshold, relaxation, allodynia, and hyperalgesia. The electron trapping/detrapping to/from the traps in the NZO layer is responsible for these nociceptive properties. This kind of NZO-based device produces a multifunctional nociceptor performance that is essential for applications in artificial intelligence systems, such as neural integrated devices with nanometer-sized features.

Neuromorphic functions with a polyelectrolyte-confined fluidic memristor

Reproducing ion channel-based neural functions with artificial fluidic systems has long been an aspirational goal for both neuromorphic computing and biomedical applications. In this study, neuromorphic functions were successfully accomplished with a polyelectrolyte-confined fluidic memristor (PFM), in which confined polyelectrolyte-ion interactions contributed to hysteretic ion transport, resulting in ion memory effects. Various electric pulse patterns were emulated by PFM with ultralow energy consumption. The fluidic property of PFM enabled the mimicking of chemical-regulated electric pulses. More importantly, chemical-electric signal transduction was implemented with a single PFM. With its structural similarity to ion channels, PFM is versatile and easily interfaces with biological systems, paving a way to building neuromorphic devices with advanced functions by introducing rich chemical designs.

The Unexpected Role of Cations in the Self-Assembly of Positively Charged Amphiphiles at Liquid/Liquid Interfaces

Conventional wisdom suggests that cations play a minimal role in the assembly of cationic amphiphiles. Here, we show that at liquid/liquid (L/L) interfaces, specific cation effects can modulate the assemblies of hydrophobic tails in an oil phase despite being attached to cationic headgroups in the aqueous phase. We used oligo-dimethylsiloxane (ODMS) methyl imidazolium amphiphiles to identify these specific interactions at hexadecane/aqueous interfaces. Small cations, such as Li⁺, bind to the O atoms in the ODMS tail and pin it to the interface, thereby imposing a kinked conformation─as evidenced by vibrational sum frequency generation spectroscopy and molecular dynamics simulations. While larger Cs⁺ ions more readily partition to the interface, they do not form analogous complexes. Our data not only point to ways for controlling amphiphile structure at L/L interfaces but also suggest a means for the separation of Li⁺, or related applications, in soft-matter electronics.

Extended and Generic Higher-Order Elements for MEMS Modeling

State-dependent resistors, capacitors, and inductors are a common part of many smart engineering solutions, e.g., in MEMS (Micro-Electro-Mechanical Systems) sensors and actuators, Micro/NanoMachines, or biomimetic systems. These memory elements are today modeled as generic and extended memristors (MR), memcapacitors (MC), and meminductors (ML), which are more general versions of classical MR, MC, and ML from the infinite set of the fundamental elements of electrical engineering, known as Higher-Order Elements (HOEs). It turns out that models of many complex phenomena in MEMS cannot be constructed only from classical and state-dependent elements such as R, L, and C, but that other HOEs with generalized behavior should also be used. Thus, in this paper, generic and extended versions of HOEs are introduced, overcoming the existing limitation to MR, MC, and ML elements. The relevant circuit theorems are formulated, which generalize the well-known theorems of classical memory elements, and their application to model complex processes of various physical natures in MEMS is shown.

Homogeneous hybrid droplet interface bilayers assembled from binary mixtures of DPhPC phospholipids and PB-b-PEO diblock copolymers

Hybrid membranes built from phospholipids and amphiphilic block copolymers seek to capitalize on the benefits of both constituents for constructing biomimetic interfaces with improved performance. However, hybrid membranes have not been formed or studied using the droplet interface bilayer (DIB) method, an approach that offers advantages for revealing nanoscale changes in membrane structure and mechanics and offers a path toward assembling higher-order tissues. We report on hybrid droplet interface bilayers (hDIBs) formed in hexadecane from binary mixtures of synthetic diphytanoyl phosphatidylcholine (DPhPC) lipids and low molecular weight 1,2 polybutadiene-b-polyethylene oxide (PBPEO) amphiphilic block copolymers and use electrophysiology measurements and imaging to assess the effects of PBPEO in the membrane. This work reveals that hDIBs containing up to 15 mol% PBPEO plus DPhPC are homogeneously mixtures of lipids and polymers, remain highly resistive to ion transport, and are stable-including under applied voltage. Moreover, they exhibit hydrophobic thicknesses similar to DPhPC-only bilayers, but also have significantly lower values of membrane tension. These characteristics coincide with reduced energy of adhesion between droplets and the formation of alamethicin ion channels at significantly lower threshold voltages, demonstrating that even moderate amounts of amphiphilic block copolymers in a lipid bilayer provide a route for tuning the physical properties of a biomimetic membrane.

Heterosynaptic plasticity in biomembrane memristors controlled by pH

Abstract

In biology, heterosynaptic plasticity maintains homeostasis in synaptic inputs during associative learning and memory, and initiates long-term changes in synaptic strengths that nonspecifically modulate different synapse types. In bioinspired neuromorphic circuits, heterosynaptic plasticity may be used to extend the functionality of two-terminal, biomimetic memristors. In this article, we explore how changes in the pH of droplet interface bilayer aqueous solutions modulate the memristive responses of a lipid bilayer membrane in the pH range 4.97-7.40. Surprisingly, we did not find conclusive evidence for pH-dependent shifts in the voltage thresholds (V*) needed for alamethicin ion channel formation in the membrane. However, we did observe a clear modulation in the dynamics of pore formation with pH in time-dependent, pulsed voltage experiments. Moreover, at the same voltage, lowering the pH resulted in higher steady-state currents because of increased numbers of conductive peptide ion channels in the membrane. This was due to increased partitioning of alamethicin monomers into the membrane at pH 4.97, which is below the pKa (~5.3-5.7) of carboxylate groups on the glutamate residues of the peptide, making the monomers more hydrophobic. Neutralization of the negative charges on these residues, under acidic conditions, increased the concentration of peptide monomers in the membrane, shifting the equilibrium concentrations of peptide aggregate assemblies in the membrane to favor greater numbers of larger, increasingly more conductive pores. It also increased the relaxation time constants for pore formation and decay, and enhanced short-term facilitation and depression of the switching characteristics of the device. Modulating these thresholds globally and independently of alamethicin concentration and applied voltage will enable the assembly of neuromorphic computational circuitry with enhanced functionality.

Impact statement

We describe how to use pH as a modulatory "interneuron" that changes the voltage-dependent memristance of alamethicin ion channels in lipid bilayers by changing the structure and dynamical properties of the bilayer. Having the ability to independently control the threshold levels for pore conduction from voltage or ion channel concentration enables additional levels of programmability in a neuromorphic system. In this article, we note that barriers to conduction from membrane-bound ion channels can be lowered by reducing solution pH, resulting in higher currents, and enhanced short-term learning behavior in the form of paired-pulse facilitation. Tuning threshold values with environmental variables, such as pH, provide additional training and learning algorithms that can be used to elicit complex functionality within spiking neural networks.

Graphical abstract

Supplementary information

The online version contains supplementary material available at 10.1557/s43577-022-00344-z.

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.

Challenges and opportunities in achieving the full potential of droplet interface bilayers

Model membranes can be used to elucidate the intricacies of the chemical processes that occur in cell membranes, but the perfectly biomimetic, yet bespoke, model membrane has yet to be built. Droplet interface bilayers are a new type of model membrane able to mimic some features of real cell membranes better than traditional models, such as liposomes and black lipid membranes. In this Perspective, we discuss recent work in the field that is starting to showcase the potential of these model membranes to enable the quantification of membrane processes, such as the behaviour of protein transporters and the prediction of in vivo drug movement, and their use as scaffolds for electrophysiological measurements. We also highlight the challenges that remain to enable droplet interface bilayers to achieve their full potential as artificial cells, and as biological analytical platforms to quantify molecular transport.

Artificial Neural Pathway Based on a Memristor Synapse for Optically Mediated Motion Learning

Animals execute intelligent and efficient interactions with their surroundings through neural pathways, exhibiting learning, memory, and cognition. Artificial autonomous devices that generate self-optimizing feedback mimicking biological systems are essential in pursuing future intelligent robots. Here, we report an artificial neural pathway (ANP) based on a memristor synapse to emulate neuromorphic learning behaviors. In our ANP, optical stimulations are detected and converted into electrical signals through a flexible perovskite photoreceptor. The acquired electrical signals are further processed in a zeolitic imidazolate frameworks-8 (ZIF-8)-based memristor device. By controlling the growth of the ZIF-8 nanoparticles, the conductance of the memristor can be finely modulated with electrical stimulations to mimic the modulation of synaptic plasticity. The device is employed in the ANP to implement synaptic functions of learning and memory. Subsequently, the synaptic feedbacks are used to direct a robotic arm to perform responding motions. Upon repeatedly "reviewing" the optical stimulation, the ANP is able to learn, memorize, and complete the specific motions. This work provides a promising strategy toward the design of intelligent autonomous devices and bioinspired robots through memristor-based systems.

Squeezing Out Interfacial Solvation: The Role of Hydrogen-Bonding in the Structural and Orientational Freedom of Molecular Self-Assembly

Bioinspired membrane molecules with improved physical properties and enhanced stability can serve as functional models for conventional lipid or amphiphilic species. Importantly, these molecules can also provide new insights into emergent phenomena that manifest during self-assembly at interfaces. Here, we elucidate the structural response and mechanistic steps underlying the self-assembly of the amphiphilic, charged oligodimethylsiloxane imidazolium cation (ODMS-MIM⁺) at the air-aqueous interface using Langmuir trough methods with coincident surface-specific vibrational sum-frequency generation (SFG) spectroscopy. We find evidence for a new compression-induced desolvation step that precedes commonly known disordered-to-ordered phase transitions to form nanoscopic assemblies. The experimental data was supported by atomistic molecular dynamics (MD) simulations to provide a detailed mechanistic picture underlying the assembly and the role of water in these phase transitions. The sensitivity of the hydrophobic ODMS tail conformations to compression─owing to distinct water-ODMS interactions and tail-tail solvation properties─offers new strategies for the design of interfaces that can be further used to develop soft-matter electronics and low-dimensional materials using physical and chemical controls.

Ion Pairing and Molecular Orientation at Liquid/Liquid Interfaces: Self-Assembly and Function

Molecular orientation plays a pivotal role in defining the functionality and chemistry of interfaces, yet accurate measurements probing this important feature are few, due, in part, to technical and analytical limitations in extracting information from molecular monolayers. For example, buried liquid/liquid interfaces, where a complex and poorly understood balance of inter- and intramolecular interactions impart structural constraints that facilitate the formation of supramolecular assemblies capable of new functions, are difficult to probe experimentally. Here, we use vibrational sum-frequency generation spectroscopy, numerical polarization analysis, and atomistic molecular dynamics simulations to probe molecular orientations at buried oil/aqueous interfaces decorated with amphiphilic oligomers. We show that the orientation of self-assembled oligomers changes upon the addition of salts in the aqueous phase. The evolution of these structures can be described by competitive ion effects in the aqueous phase altering the orientations of the tails extending into the oil phase. These specific anionic effects occur via interfacial ion pairing and associated changes in interfacial solvation and hydrogen-bonding networks. These findings provide more quantitative insight into orientational changes encountered during self-assembly and pave the way for the design of functional interfaces for chemical separations, neuromorphic computing applications, and related biomimetic systems.

Opportunities for neuromorphic computing algorithms and applications

Neuromorphic computing technologies will be important for the future of computing, but much of the work in neuromorphic computing has focused on hardware development. Here, we review recent results in neuromorphic computing algorithms and applications. We highlight characteristics of neuromorphic computing technologies that make them attractive for the future of computing and we discuss opportunities for future development of algorithms and applications on these systems.

Locally superengineered cascade recognition–quantification zones in nanochannels for sensitive enantiomer identification

As an intriguing and intrinsic feature of life, chirality is highly associated with many significant biological processes. Simultaneous recognition and quantification of enantiomers remains a major challenge. Here, a sensitive enantiomer identification device is developed on TiO₂ nanochannels via the design of cascade recognition–quantification zones along the nanochannels. In this system, β-cyclodextrin (β-CD) is self-assembled on one side of the nanochannels for the selective recognition of enantiomers; CuMOFs are designed as the target-responsive partners on the other side of the nanochannels for the quantification of enantiomers that pass through the nanochannels. As a proof-of-principle of the cascade design, arginine (Arg) enantiomers are tested as the identification targets. The l-Arg molecules selectively bind in the recognition zone; d-Arg molecules pass through the recognition zone and then interact with the quantification zone via a specialized reduction reaction. As verified by nanofluidic simulations, because of the confinement effect of nanoscale channels combined with the condensation effect of porous structure, the in situ reaction in the quantification zone contributes to an unprecedented variation in transmembrane K⁺ flux, leading to an improved identification signal. This novel cascade-zone nanochannel membrane provides a smart strategy to design multifunctional nanofluidic devices.

A design of the cascade recognition–quantification zone is developed along TiO₂ nanochannels. The asymmetric nanochannels exhibit a predominant sensitivity and selectivity for enantiomer discrimination.

Bio-memristors based on silk fibroin

Bio-memristors constitute candidates for the next generation of non-volatile storage and bionic synapses due to their biocompatibility, environmental benignity, sustainability, flexibility, degradability, and impressive memristive performance. Silk fibroin (SF), a natural and abundant biomaterial with excellent mechanical, optical, electrical, and structure-adjustable properties as well as being easy to process, has been utilized and shown to have potential in the construction of bio-memristors. Here, we first summarize the fundamental mechanisms of bio-memristors based on SF. Then, the latest achievements and developments of pristine and composited SF-based memristors are highlighted, followed by the integration of memristive devices. Finally, the challenges and insights associated with SF-based bio-memristors are presented. Advances in SF-based bio-memristors will open new avenues in the design and integration of high-performance bio-integrated systems and facilitate their application in logic operations, complex circuits, and neural networks.

Tailored Chiral Copper Selenide Nanochannels for Ultrasensitive Enantioselective Recognition and Detection

We constructed a tailorable membrane channel system consisting of penicillamine molecules intercalated in copper selenide nanoparticles (Cu_2-x Se NPs), which exhibited circular dichroism (CD) bands in the near infrared region (CD, 800-1600 nm) with a maximum intensity of 164.5 mdeg at 1440 nm. The chiral ligand hybridized to the surface of achiral Cu_2-x Se NPs by breaking the intrinsic symmetry of Cu_2-x Se NPs and further large-scale assembly induced strong optical activity. The fabricated multilayer chiral membrane achieved an increased rectification ratio (RR) up to 114. The integration of penicillamine allowed for high enantioselective recognition against naproxen,which displayed high sensitivity with a limit of detection (LOD) as low as 0.027 nM.

The role of temperature in the formation of human-mimetic artificial cell membranes using droplet interface bilayers (DIBs)

Droplet interface bilayers (DIBs) have recently started to be used as human-mimetic artificial cell membranes. DIBs are bilayer sections created at the interface of two aqueous droplets, such that one droplet can be used as a donor compartment and the other as an acceptor compartment for the quantification of molecular transport across the artificial cell membrane. However, synthetic phospholipids are overwhelmingly used to create DIBs instead of naturally derived phospholipids, even though the diverse distribution of phospholipids in the latter is more biomimetic. We present the first systematic study of the role of temperature in DIB formation, which shows that the temperature at which DIBs are formed is a key parameter for the formation of DIBs using naturally derived phospholipids in a microfluidic platform. The phospholipids that are most abundant in mammalian cell membranes (phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), and phosphatidylinositol (PI)) only form DIBs when the temperature is above the phase transition temperature (T_m). Similarly, DIB formation usually only occurs above the highest T_m of a single phospholipid in a bespoke formulation. In addition, we show a new phenomenon wherein the DIB "melts" without disintegrating for bilayers formed predominantly of phospholipids that occupy cylindrical spaces. We also demonstrate differences in DIB formation rates as well as permeability of these biomimetic membranes. Given the difficulties associated with making DIBs using naturally derived phospholipids, we anticipate this work will illuminate the role of phospholipid phase transition in mono- and bilayer formation and lay the foundation for DIBs to be used as human-mimetic artificial cell membranes.

Ion Pairing Mediates Molecular Organization Across Liquid/Liquid Interfaces

Liquid/liquid interfaces play a central role in scientific fields ranging from nanomaterial synthesis and soft matter electronics to nuclear waste remediation and chemical separations. This diversity of functions arises from an interface's ability to respond to changing conditions in its neighboring bulk phases. Understanding what drives this interfacial flexibility can provide novel avenues for designing new functional interfaces. However, limiting this progress is an inadequate understanding of the subtle intermolecular and interphase interactions taking place at the molecular level. Here, we use surface-specific vibrational sum frequency generation spectroscopy combined with atomistic molecular dynamics simulations to investigate the self-assembly and structure of model ionic oligomers consisting of an oligodimethylsiloxane (ODMS) tail covalently attached to a positively charged methyl imidazolium (MIM⁺) head group at buried oil/aqueous interfaces. We show how the presence of seemingly innocuous salts can impart dramatic changes to the ODMS tail conformations in the oil phase via specific ion effects and ion-pairing interactions taking place in the aqueous phase. These specific ion interactions are shown to drive enhanced amphiphile adsorption, induce morphological changes, and disrupt emergent hydrogen-bonding structures at the interface. Tuning these interactions allows for independent control over the oligomer structure in the oil phase versus interfacial population changes and represents key mechanistic insight that is needed to control chemical reactions at liquid/liquid interfaces.

Low-Power and Tunable-Performance Biomemristor Based on Silk Fibroin

Biomemristors have attracted significant attention because of their potential applications in logic operations, nonvolatile memory, and synaptic emulators, thus leading to the urgent need to improve memristive performance. In this work, a silk fibroin (SF)-based memristor, integrated with both low power and low operating current simultaneously, has been reported. Doping the SF with Ag and an ethanol-based post-treatment promote microcrystal formation in the bulk of the SF. This induces carrier transport along fixed, short paths and results in a low set voltage, low operating current, and high memristive stability. Such performances can greatly reduce power consumption and heat generation, beneficial for the accuracy and durability of memristor devices. The memristive mechanism of SF-based memristors with different Ag contents is the space-charge-limited conduction (SCLC) mechanism. In addition, the nonlinear transmission property of SF-based memristors suggests useful applications in bioelectronics.

Memristor-Based Neural Network Circuit of Emotion Congruent Memory With Mental Fatigue and Emotion Inhibition

Most memristor-based neural networks only consider a single mode of memory or emotion, but ignore the relationship between emotion and memory. In this paper, a memristor-based neural network circuit of emotion congruent memory is proposed and verified by the simulation results. The designed circuit consists of a memory module, an emotion module and an association neuron module. Varieties of memory and emotion functions are considered. The functions such as learning, forgetting, variable rate and emotion generation are implemented by the circuit. Furthermore, mental fatigue and emotion inhibition which are two important self-protective measures of the brain are realized in this paper on the basis of emotion congruent memory. Finally, the paper also considers the congruence between emotion and memory materials and the regulation of emotion on memory. The neural network circuit of emotion congruent memory can provide more references for the application of memristor.

Threshold switching memristor-based stochastic neurons for probabilistic computing

Biological neurons exhibit dynamic excitation behavior in the form of stochastic firing, rather than stiffly giving out spikes upon reaching a fixed threshold voltage, which empowers the brain to perform probabilistic inference in the face of uncertainty. However, owing to the complexity of the stochastic firing process in biological neurons, the challenge of fabricating and applying stochastic neurons with bio-realistic dynamics to probabilistic scenarios remains to be fully addressed. In this work, a novel CuS/GeSe conductive-bridge threshold switching memristor is fabricated and singled out to realize electronic stochastic neurons, which is ascribed to the similarity between the stochastic switching behavior observed in the device and that of biological ion channels. The corresponding electric circuit of a stochastic neuron is then constructed and the probabilistic firing capacity of the neuron is utilized to implement Bayesian inference in a spiking neural network (SNN). The application prospects are demonstrated on the example of a tumor diagnosis task, where common fatal diagnostic errors of a conventional artificial neural network are successfully circumvented. Moreover, in comparison to deterministic neuron-based SNNs, the stochastic neurons enable SNNs to deliver an estimate of the uncertainty in their predictions, and the fidelity of the judgement is drastically improved by 81.2%.

Synaptic Iontronic Devices for Brain-Mimicking Functions: Fundamentals and Applications

Inspired by the information transmission mechanism in the central nervous systems of life, synapse-mimicking devices have been designed and fabricated for the purpose of breaking the bottleneck of von Neumann architecture and realizing the construction of effective hardware-based artificial intelligence. In this case, synaptic iontronic devices, dealing with current information with ions instead of electrons, have attracted enormous scientific interests owing to their unique characteristics provided by ions, such as the designability of charge carriers and the diversity of chemical regulation. Herein, the basic conception, working mechanism, performance metrics, and advanced applications of synaptic iontronic devices based on three-terminal transistors and two-terminal memristors are systematically reviewed and comprehensively discussed. This Review provides a prospect on how to realize artificial synaptic functions based on the regulation of ions and raises a series of further challenges unsolved in this area.

Reliable organic memristors for neuromorphic computing by predefining a localized ion-migration path in crosslinkable polymer

In flexible neuromorphic systems for realizing artificial intelligence, organic memristors are essential building blocks as artificial synapses to perform information processing and memory. Despite much effort to implement artificial neural networks (ANNs) using organic memristors, the reliability of these devices is inherently hampered by global ion transportation and arbitrary growth of conductive filaments (CFs). As a result, the performance of ANNs is restricted. Herein, a novel concept for confining CF growth in organic memristors is demonstrated by exploiting the unique functionality of crosslinkable polymers. This can be achieved by predefining the localized ion-migration path (LIP) in crosslinkable polymers. In the proposed organic memristor, metal cations are locally transported along the LIP. Thus, CF growth is achieved only in a confined region. A flexible memristor with an LIP exhibits a vastly improved reliability and uniformity, and it is capable of operating with high mechanical and electrical endurance. Moreover, neuromorphic arrays based on the proposed memristor exhibit 96.3% learning accuracy, which is comparable to the ideal software baseline. The proposed concept of predefining the LIP in organic memristors is expected to provide novel platforms for the advance of flexible electronics and to realize a variety of practical neural networks for artificial intelligence applications.

Realization of Biomimetic Synaptic Functions in a One-Cell Organic Resistive Switching Device Using the Diffusive Parameter of Conductive Filaments

Toward the successful development of artificial intelligence, artificial synapses based on resistive switching devices are essential ingredients to perform information processing in spiking neural networks. In neural processes, synaptic plasticity related to the history of neuron activity plays a critical role during learning. In resistive switching devices, it is barely possible to emulate both short-term plasticity and long-term plasticity due to the uncontrollable dynamics of the conductive filaments (CFs). Despite extensive effort to realize synaptic plasticity in such devices, it is still challenging to achieve reliable synaptic functions due to the overgrowth of CFs in a random fashion. Herein, we propose an organic resistive switching device with bio-realistic synaptic functions by adjusting the CF diffusive parameter. In the proposed device, complete synaptic plasticity provides the history-dependent change in the conductance. Moreover, the homeostatic feedback, which resembles the biological process, regulates CF growth in our device, which enhances the reliability of synaptic plasticity. This novel concept for realizing synaptic functions in organic resistive switching devices may provide a physical platform to advance the fundamental understanding of learning and memory mechanisms and develop a variety of neural circuits and neuromorphic systems that can be linked to artificial intelligence and next-generation computing paradigm.

Plasticity and Adaptation in Neuromorphic Biohybrid Systems

Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel "biohybrid" experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering.

Bioinspired Ionic Sensory Systems: The Successor of Electronics

All biological systems, including animals and plants, communicate in a language of ions and small molecules, while the modern information infrastructures and technologies rely on a language of electrons. Although electronics and bioelectronics have made great progress in the past several decades, they still face the disadvantage of signal transformation when communicating with biology. To narrow the gap between biological systems and artificial-intelligence systems, bioinspired ion-transport-based sensory systems should be developed as successor of electronics, since they can emulate biological functionality more directly and communicate with biology seamlessly. Herein, the essential principles of (accurate) ion transport are introduced, and the recent progress in the development of three elements of an ionic sensory system is reviewed: ionic sensors, ionic processors, and ionic interfaces. The current challenges and future developments of ion-transport-based sensory systems are also discussed.

Solution-processed inorganic δ-phase CsPbI3 electronic synapses with short- and long-term plasticity in a crossbar array structure

Electronic synapses based on memristive devices can potentially open a niche area for neuromorphic computing by replicating the function of biological synapses with high fidelity. Recently, two-terminal memristors based on halide perovskites have demonstrated outstanding memristive properties and a variety of synaptic characteristics, combining with their additional advantages such as a solution-processed fabrication method and low crystalline temperature. However, the concerns over the chemical and phase stability of halide perovskites greatly hinder their practical applications. In this study, by using a simple single-step spin-coating method, we report artificial synapses with superior ambient stability (>90 days under ambient conditions) based on fully inorganic nonperovskite δ-phase CsPbI3 in a cross-bar array architecture. The threshold switching memristive device exhibits a moderate ON/OFF ratio, a relatively low operation voltage (0.3 V) and high endurance (>5 × 105). More importantly, the electronic device can emulate synaptic characteristics such as short-term plasticity, paired-pulse facilitation, and the transition from short-term memory to long-term memory with a high output signal-to-noise ratio (>102). This work represents the first record for artificial synapses based on nonperovskite CsPbI3 and will be a step toward achieving low-cost and high-density practical synapse arrays.

Artificial Biosystems by Printing Biology

The continuous progress of printing technologies over the past 20 years has fueled the development of a plethora of applications in materials sciences, flexible electronics, and biotechnologies. More recently, printing methodologies have started up to explore the world of Artificial Biology, offering new paradigms in the direct assembly of Artificial Biosystems (small condensates, compartments, networks, tissues, and organs) by mimicking the result of the evolution of living systems and also by redesigning natural biological systems, taking inspiration from them. This recent progress is reported in terms of a new field here defined as Printing Biology, resulting from the intersection between the field of printing and the bottom up Synthetic Biology. Printing Biology explores new approaches for the reconfigurable assembly of designed life-like or life-inspired structures. This work presents this emerging field, highlighting its main features, i.e., printing methodologies (from 2D to 3D), molecular ink properties, deposition mechanisms, and finally the applications and future challenges. Printing Biology is expected to show a growing impact on the development of biotechnology and life-inspired fabrication.