Highly Bionic Neurotransmitter-Communicated Neurons Following Integrate-and-Fire Dynamics.

In biological neural networks, chemical communication follows the reversible integrate-and-fire (I&F) dynamics model, enabling efficient, anti-interference signal transport. However, existing artificial neurons fail to follow the I&F model in chemical communication, causing irreversible potential accumulation and neural system dysfunction. Herein, we develop a supercapacitively gated artificial neuron that mimics the reversible I&F dynamics model. Upon upstream neurotransmitters, an electrochemical reaction occurs on a graphene nanowall (GNW) gate electrode of artificial neurons. Charging and discharging the supercapacitive GNWs mimic membrane potential accumulation and recovery, realizing highly efficient chemical communication upon use of acetylcholine down to 2 × 10-10 M. By combining artificial chemical synapses with axon-hillock circuits, the output of neural spikes is realized. With the same neurotransmitter and I&F dynamics, the artificial neuron establishes chemical communication with other artificial neurons and living cells, holding promise as a basic unit to construct a neural network with compatibility to organisms for artificial intelligence and deep human-machine fusion.

Emerging memristive neurons for neuromorphic computing and sensing.

Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.

A complementary resistive switching neuron.

The complementary resistive switching (CRS) memristor has originally been proposed for use as the storage element or artificial synapse in large-scale crossbar array with the capability of solving the sneak path problem, but its usage has mainly been hampered by the inherent destructiveness of the read operation (switching '1' state to 'ON' or '0' state). Taking a different perspective on this 'undesired' property, we here report on the inherent behavioral similarity between the CRS memristor and a leaky integrate-and-fire (LIF) neuron which is another basic neural computing element, in addition to synapse. In particular, the mechanism behind the undesired read destructiveness for storage element and artificial synapse can be exploited to naturally realize the LIF and the ensuing spontaneous repolarization processes, followed by a refractory period. By means of this biological similarity, we demonstrate a Pt/Ta2O5-x/TaOy/Ta CRS memristor that can exhibit these neuronal behaviors and perform various fundamental neuronal operations, including additive/subtractive operations and coincidence detection. These results suggest that the CRS neuron, with its bio-interpretability, is a useful addition to the family of memristive neurons.

A Tantalum Disulfide Charge-Density-Wave Stochastic Artificial Neuron for Emulating Neural Statistical Properties.

Artificial neuronal devices that functionally resemble biological neurons are important toward realizing advanced brain emulation and for building bioinspired electronic systems. In this Communication, the stochastic behaviors of a neuronal oscillator based on the charge-density-wave (CDW) phase transition of a 1T-TaS2 thin film are reported, and the capability of this neuronal oscillator to generate spike trains with statistical features closely matching those of biological neurons is demonstrated. The stochastic behaviors of the neuronal device result from the melt-quench-induced reconfiguration of CDW domains during each oscillation cycle. Owing to the stochasticity, numerous key features of the Hodgkin-Huxley description of neurons can be realized in this compact two-terminal neuronal oscillator. A statistical analysis of the spike train generated by the artificial neuron indicates that it resembles the neurons in the superior olivary complex of a mammalian nervous system, in terms of its interspike interval distribution, the time-correlation of spiking behavior, and its response to acoustic stimuli.

Artificial Neuron and Synapse Devices Based on 2D Materials.

Neuromorphic systems, which emulate neural functionalities of a human brain, are considered to be an attractive next-generation computing approach, with advantages of high energy efficiency and fast computing speed. After these neuromorphic systems are proposed, it is demonstrated that artificial synapses and neurons can mimic neural functions of biological synapses and neurons. However, since the neuromorphic functionalities are highly related to the surface properties of materials, bulk material-based neuromorphic devices suffer from uncontrollable defects at surfaces and strong scattering caused by dangling bonds. Therefore, 2D materials which have dangling-bond-free surfaces and excellent crystallinity have emerged as promising candidates for neuromorphic computing hardware. First, the fundamental synaptic behavior is reviewed, such as synaptic plasticity and learning rule, and requirements of artificial synapses to emulate biological synapses. In addition, an overview of recent advances on 2D materials-based synaptic devices is summarized by categorizing these into various working principles of artificial synapses. Second, the compulsory behavior and requirements of artificial neurons such as the all-or-nothing law and refractory periods to simulate a spike neural network are described, and the implementation of 2D materials-based artificial neurons to date is reviewed. Finally, future challenges and outlooks of 2D materials-based neuromorphic devices are discussed.

Halide Perovskites-Based Diffusive Memristors for Artificial Mechano-Nociceptive System.

Numerous efforts for emulating organ systems comprised of multiple functional units have driven substantial advancements in bio-realistic electronics and systems. The resistance change behavior observed in diffusive memristors shares similarities with the potential change in biological neurons. Here, the diffusive threshold switching phenomenon in Ag-incorporated organometallic halide perovskites is utilized to demonstrate the functions of afferent neurons. Halide perovskites-based diffusive memristors show a low threshold voltage of ≈0.2 V with little variation, attributed to the facile migration of Ag ions uniformly dispersed within the halide matrix. Based on the reversible and reliable volatile threshold switching, the memristors successfully demonstrate fundamental nociceptive functions including threshold firing, relaxation, and sensitization. Furthermore, to replicate the biological mechano-nociceptive phenomenon at a system level, an artificial mechano-nociceptive system is built by integrating a diffusive memristor with a force-sensing resistor. The presented system is capable of detecting and discerning the detrimental impact caused by a heavy steel ball, effectively exhibiting the corresponding sensitization response. By further extending the single nociceptive system into a 5 × 5 array, successful stereoscopic nociception of uneven impulses is achieved in the artificial skin system through array-scale sensitization. These results represent significant progress in the field of bio-inspired electronics and systems.

Recent Progress in Artificial Neurons for Neuromodulation.

Driven by the rapid advancement and practical implementation of biomaterials, fabrication technologies, and artificial intelligence, artificial neuron devices and systems have emerged as a promising technology for interpreting and transmitting neurological signals. These systems are equipped with multi-modal bio-integrable sensing capabilities, and can facilitate the benefits of neurological monitoring and modulation through accurate physiological recognition. In this article, we provide an overview of recent progress in artificial neuron technology, with a particular focus on the high-tech applications made possible by innovations in material engineering, new designs and technologies, and potential application areas. As a rapidly expanding field, these advancements have a promising potential to revolutionize personalized healthcare, human enhancement, and a wide range of other applications, making artificial neuron devices the future of brain-machine interfaces.

Artificial intelligence and treatment algorithms in spine surgery.

Artificial intelligence (AI) is a set of theories and techniques in which machines are used to simulate human intelligence with complex computer programs. The various machine learning (ML) methods are a subtype of AI. They originate from computer science and use algorithms established from analyzing a database to accomplish certain tasks. Among these methods are decision trees or random forests, support vector machines along with artificial neural networks. Convolutive neural networks were inspired from the visual cortex; they process combinations of information used in image or voice recognition. Deep learning (DL) groups together a set of ML methods and is useful for modeling complex relationships with a high degree of abstraction by using multiple layers of artificial neurons. ML techniques have a growing role in spine surgery. The main applications are the segmentation of intraoperative images for surgical navigation or robotics used for pedicle screw placement, the interpretation of images of intervertebral discs or full spine radiographs, which can be automated using ML algorithms. ML techniques can also be used as aids for surgical decision-making in complex fields, such as preoperative evaluation of adult spinal deformity. ML algorithms "learn" from large clinical databases. They make it possible to establish the intraoperative risk level and make a prognosis on how the postoperative functional scores will change over time as a function of the patient profile. These applications open a new path relative to standard statistical analyses. They make it possible to explore more complex relationships with multiple indirect interactions. In the future, AI algorithms could have a greater role in clinical research, evaluating clinical and surgical practices, and conducting health economics analyses.

Electrochemical-Memristor-Based Artificial Neurons and Synapses-Fundamentals, Applications, and Challenges.

Artificial neurons and synapses are considered essential for the progress of the future brain-inspired computing, based on beyond von Neumann architectures. Here, a discussion on the common electrochemical fundamentals of biological and artificial cells is provided, focusing on their similarities with the redox-based memristive devices. The driving forces behind the functionalities and the ways to control them by an electrochemical-materials approach are presented. Factors such as the chemical symmetry of the electrodes, doping of the solid electrolyte, concentration gradients, and excess surface energy are discussed as essential to understand, predict, and design artificial neurons and synapses. A variety of two- and three-terminal memristive devices and memristive architectures are presented and their application for solving various problems is shown. The work provides an overview of the current understandings on the complex processes of neural signal generation and transmission in both biological and artificial cells and presents the state-of-the-art applications, including signal transmission between biological and artificial cells. This example is showcasing the possibility for creating bioelectronic interfaces and integrating artificial circuits in biological systems. Prospectives and challenges of the modern technology toward low-power, high-information-density circuits are highlighted.

Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications.

Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.

Artificial Intelligence and Advanced Materials.

Artificial intelligence (AI) is gaining strength, and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems, and processes can be devised and optimized thanks to machine learning (ML) techniques, and such progress can be turned into innovative computing platforms. Future materials scientists will profit from understanding how ML can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by computation to account for the origins, procedures, and applications of AI. ML and its methods are reviewed to provide basic knowledge of its implementation and its potential. The materials and systems used to implement AI with electric charges are finding serious competition from other information-carrying and processing agents. The impact these techniques have on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materials and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom, as exemplified by the power to discover unheard of materials or physical laws buried in data.

Highly Reliable Threshold Switching Characteristics of Surface-Modulated Diffusive Memristors Immune to Atmospheric Changes.

Active cation-based diffusive memristors featuring essentially volatile threshold switching have been proposed for novel applications, such as a selector in a one-selector-and-one-resistor structure and signal generators in neuromorphic computing. However, the high variability of the switching behavior, which results from the high electroforming voltage, external environmental conditions, and transition to the non-volatile switching mode in a high-current range, is considered a major impediment to such applications. Herein, for the first time, we developed a highly reliable threshold switching device immune to atmospheric changes based on an ultraviolet-ozone (UVO)-treated diffusive memristor consisting of Ag and SiO2 nanorods (NRs). UVO treatment forms a stable water reservoir on the surface of SiO2 NRs, facilitating the redox reaction and ion migration of Ag. Consequently, diffusive memristors possess reliable switching characteristics, including electroforming-free, repeatable, and consistent switching with resistance to changes in ambient conditions and compliance levels during operation. We demonstrated that our approach is suitable for various metal oxides and can be used in numerous applications.

Controllable SiOx Nanorod Memristive Neuron for Probabilistic Bayesian Inference.

Modern artificial neural network technology using a deterministic computing framework is faced with a critical challenge in dealing with massive data that are largely unstructured and ambiguous. This challenge demands the advances of an elementary physical device for tackling these uncertainties. Here, we designed and fabricated a SiOx nanorod memristive device by employing the glancing angle deposition (GLAD) technique, suggesting a controllable stochastic artificial neuron that can mimic the fundamental integrate-and-fire signaling and stochastic dynamics of a biological neuron. The nanorod structure provides the random distribution of multiple nanopores all across the active area, capable of forming a multitude of Si filaments at many SiOx nanorod edges after the electromigration process, leading to a stochastic switching event with very high dynamic range (≈5.15 × 1010 ) and low energy (≈4.06 pJ). Different probabilistic activation (ProbAct) functions in a sigmoid form are implemented, showing its controllability with low variation by manufacturing and electrical programming schemes. Furthermore, as an application prospect, based on the suggested memristive neuron, we demonstrated the self-resting neural operation with the local circuit configuration and revealed probabilistic Bayesian inferences for genetic regulatory networks with low normalized mean squared errors (≈2.41 × 10-2 ) and its robustness to the ProbAct variation.

Organic Neuroelectronics: From Neural Interfaces to Neuroprosthetics.

Requirements and recent advances in research on organic neuroelectronics are outlined herein. Neuroelectronics such as neural interfaces and neuroprosthetics provide a promising approach to diagnose and treat neurological diseases. However, the current neural interfaces are rigid and not biocompatible, so they induce an immune response and deterioration of neural signal transmission. Organic materials are promising candidates for neural interfaces, due to their mechanical softness, excellent electrochemical properties, and biocompatibility. Also, organic nervetronics, which mimics functional properties of the biological nerve system, is being developed to overcome the limitations of the complex and energy-consuming conventional neuroprosthetics that limit long-term implantation and daily-life usage. Examples of organic materials for neural interfaces and neural signal recordings are reviewed, recent advances of organic nervetronics that use organic artificial synapses are highlighted, and then further requirements for neuroprosthetics are discussed. Finally, the future challenges that must be overcome to achieve ideal organic neuroelectronics for next-generation neuroprosthetics are discussed.

Emulating the Signal Transmission in a Neural System Using Polymer Membranes.

Neurons are vital components of the brain. When stimulated by neurotransmitters at the dendrites, neurons deliver signals as changes in the membrane potential by ion movement. The signal transmission of a nervous system exhibits a high energy efficiency. These characteristics of neurons are being exploited to develop efficient neuromorphic computing systems. In this study, we develop chemical synapses for neuromorphic devices and emulate the signaling processes in a nervous system using a polymer membrane, in which the ionic permeability can be controlled. The polymer membrane comprises poly(diallyl-dimethylammonium chloride) and poly(3-sulfopropyl acrylate potassium salt), which have positive and negative charges, respectively. The ionic permeability of the polymer membrane is controlled by the injection of a neurotransmitter solution. This device emulates the signal transmission behavior of biological neurons depending on the concentration of the injected neurotransmitter solution. The proposed artificial neuronal signaling device can facilitate the development of bio-realistic neuromorphic devices.

Two-Argument Activation Functions Learn Soft XOR Operations Like Cortical Neurons.

Neurons in the brain are complex machines with distinct functional compartments that interact nonlinearly. In contrast, neurons in artificial neural networks abstract away this complexity, typically down to a scalar activation function of a weighted sum of inputs. Here we emulate more biologically realistic neurons by learning canonical activation functions with two input arguments, analogous to basal and apical dendrites. We use a network-in-network architecture where each neuron is modeled as a multilayer perceptron with two inputs and a single output. This inner perceptron is shared by all units in the outer network. Remarkably, the resultant nonlinearities often produce soft XOR functions, consistent with recent experimental observations about interactions between inputs in human cortical neurons. When hyperparameters are optimized, networks with these nonlinearities learn faster and perform better than conventional ReLU nonlinearities with matched parameter counts, and they are more robust to natural and adversarial perturbations.

Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing Applications.

Due to a rapid increase in the amount of data, there is a huge demand for the development of new memory technologies as well as emerging computing systems for high-density memory storage and efficient computing. As the conventional transistor-based storage devices and computing systems are approaching their scaling and technical limits, extensive research on emerging technologies is becoming more and more important. Among other emerging technologies, CBRAM offers excellent opportunities for future memory and neuromorphic computing applications. The principles of the CBRAM are explored in depth in this review, including the materials and issues associated with various materials, as well as the basic switching mechanisms. Furthermore, the opportunities that CBRAMs provide for memory and brain-inspired neuromorphic computing applications, as well as the challenges that CBRAMs confront in those applications, are thoroughly discussed. The emulation of biological synapses and neurons using CBRAM devices fabricated with various switching materials and device engineering and material innovation approaches are examined in depth.

A Vertical Single Transistor Neuron with Core-Shell Dual-Gate for Excitatory-Inhibitory Function and Tunable Firing Threshold Voltage.

A novel inhibitable and firing threshold voltage tunable vertical nanowire (NW) single transistor neuron device with core-shell dual-gate (CSDG) was realized and verified by TCAD simulation. The CSDG NW neuron is enclosed by an independently accessed shell gate and core gate to serve an excitatory-inhibitory transition and a firing threshold voltage adjustment, respectively. By utilizing the shell gate, the firing of specific neuron can be inhibited for winner-takes-all learning. It was confirmed that the independently accessed core gate can be used for adjustment of the firing threshold voltage to compensate random conductance variation before the learning and to fix inference error caused by unwanted synapse conductance change after the learning. This threshold voltage tuning can also be utilized for homeostatic function during the learning process. Furthermore, a myelination function which controls the transmission rate was obtained based on the inherent asymmetry between the source and drain in vertical NW structure. Finally, using the CSDG NW neuron device, a letter recognition test was conducted by SPICE simulation for a system-level validation. This multi-functional neuron device can contribute to construct a high-density monolithic SNN hardware combining with the previously developed vertical synapse MOSFET devices.

Emerging Artificial Neuron Devices for Probabilistic Computing.

In recent decades, artificial intelligence has been successively employed in the fields of finance, commerce, and other industries. However, imitating high-level brain functions, such as imagination and inference, pose several challenges as they are relevant to a particular type of noise in a biological neuron network. Probabilistic computing algorithms based on restricted Boltzmann machine and Bayesian inference that use silicon electronics have progressed significantly in terms of mimicking probabilistic inference. However, the quasi-random noise generated from additional circuits or algorithms presents a major challenge for silicon electronics to realize the true stochasticity of biological neuron systems. Artificial neurons based on emerging devices, such as memristors and ferroelectric field-effect transistors with inherent stochasticity can produce uncertain non-linear output spikes, which may be the key to make machine learning closer to the human brain. In this article, we present a comprehensive review of the recent advances in the emerging stochastic artificial neurons (SANs) in terms of probabilistic computing. We briefly introduce the biological neurons, neuron models, and silicon neurons before presenting the detailed working mechanisms of various SANs. Finally, the merits and demerits of silicon-based and emerging neurons are discussed, and the outlook for SANs is presented.

Quantum neuron with real weights.

This paper proposes a new model of a real weights quantum neuron exploiting the so-called quantum parallelism which allows for an exponential speedup of computations. The quantum neurons were trained in a classical-quantum approach, considering the delta rule to update the values of the weights in an image database of three distinct patterns. We performed classical simulations and also executed experiments in an actual small-scale quantum processor. The results of the experiments show that the proposed quantum real neuron model has a good generalisation capacity, demonstrating better accuracy than the traditional binary quantum perceptron model.