Search Results (12)

In the era of artificial intelligence, the demand for rapid and efficient data processing is growing, and traditional computing architectures are increasingly struggling to meet these needs. Against this backdrop, memristor devices, capable of mimicking the computational functions of brain neural networks, have emerged as key components in neuromorphic systems. Despite this, memristors still face many challenges in biomimetic functionality and circuit integration. In this context, a starch–glycerol-based hydrogel memristor was developed using starch as the dielectric material. The starch–glycerol–water mixture employed in this study has been widely recognized in literature as a physically cross-linked hydrogel system with a three-dimensional network, and both high water content and mechanical flexibility. This memristor demonstrates a high current switching ratio and stable threshold voltage, showing great potential in mimicking the activity of biological neurons. The device possesses the functionality of auditory neurons, not only achieving artificial spiking neuron discharge but also accomplishing the spatiotemporal summation of input information. In addition, we demonstrate the application capabilities of this artificial auditory neuron in gain modulation and in the synchronization detection of sound signals, further highlighting its potential in neuromorphic engineering applications. These results suggest that starch-based hydrogel memristors offer a promising platform for the construction of bio-inspired auditory neuron circuits and flexible neuromorphic systems. Full article

Neurodegenerative disorders include Alzheimer’s and Parkinson’s, both of which lead to progressive loss of neurons resulting in the severe loss of cognitive and motor functions. These diseases are among the heavy burdens on global healthcare systems largely because there is no cure, and current treatments apply almost entirely to controlling symptoms rather than disease progression. Recent advances in 3D printing and bioprinting technologies now open the way to overcome these challenges and form patient-specific models and therapeutical tools closely simulating the complex environment of the human brain. It then further illustrates how this technological integration with the aid of 3D printing, coupled with microfabrication and biosensing technologies, transforms drug-screening platforms as well as develops customization in medicine. For example, one can form highly intricate and multi-materially composed structures to better facilitate one’s study or test into some new therapeutic possibilities using methodologies of stereolithography and selective laser sintering. Moreover, 3D printing allows the creation of organ-on-a-chip models that simulate brain-like conditions, which may help identify specific biomarkers and evaluate new options of therapy. On the other hand, bioprinting methods based on neural cells combined with scaffolds mimicking native tissue dramatically transform regenerative medicine. New pathways in neural tissue development and implantable devices are now being brought forth, which can be tailored to the needs of individual patients. These advances bring not only greater precision in terms of the therapy that can be delivered but also 3D printing of implantable microelectrodes able to determine real-time biomarkers responsible for neurodegenerative diseases. Thus, this review highlights the robust impact that might be brought forth on the diagnosis and treatment of these neurodegenerative diseases via 3D printing technologies toward more effective management and personal solutions for healthcare. Full article

Emerging neuromorphic computing offers a promising and energy-efficient approach to developing advanced intelligent systems by mimicking the information processing modes of the human brain. Moreover, inspired by the high parallelism, fault tolerance, adaptability, and low power consumption of brain perceptual systems, replicating these efficient and intelligent systems at a hardware level will endow artificial intelligence (AI) and neuromorphic engineering with unparalleled appeal. Therefore, construction of neuromorphic devices that can simulate neural and synaptic behaviors are crucial for achieving intelligent perception and neuromorphic computing. As novel memristive devices, electrolyte-gated transistors (EGTs) stand out among numerous neuromorphic devices due to their unique interfacial ion coupling effects. Thus, the present review discusses the applications of the EGTs in neuromorphic electronics. First, operational modes of EGTs are discussed briefly. Second, the advancements of EGTs in mimicking biological synapses/neurons and neuromorphic computing functions are introduced. Next, applications of artificial perceptual systems utilizing EGTs are discussed. Finally, a brief outlook on future developments and challenges is presented. Full article

As the third-generation neural network, the spiking neural network (SNN) has become one of the most promising neuromorphic computing paradigms to mimic brain neural networks over the past decade. The SNN shows many advantages in performing classification and recognition tasks in the artificial intelligence field. In the SNN, the communication between the pre-synapse neuron (PRE) and the post-synapse neuron (POST) is conducted by the synapse. The corresponding synaptic weights are dependent on both the spiking patterns of the PRE and the POST, which are updated by spike-timing-dependent plasticity (STDP) rules. The emergence and growing maturity of spintronic devices present a new approach for constructing the SNN. In the paper, a novel SNN is proposed, in which both the synapse and the neuron are mimicked with the spin transfer torque magnetic tunnel junction (STT-MTJ) device. The synaptic weight is presented by the conductance of the MTJ device. The mapping of the probabilistic spiking nature of the neuron to the stochastic switching behavior of the MTJ with thermal noise is presented based on the stochastic Landau–Lifshitz–Gilbert (LLG) equation. In this way, a simplified SNN is mimicked with the MTJ device. The function of the mimicked SNN is verified by a handwritten digit recognition task based on the MINIST database. Full article

Neuroprostheses are neuroengineering devices that have an interface with the nervous system and supplement or substitute functionality in people with disabilities. In the collective imagination, neuroprostheses are mostly used to restore sensory or motor capabilities, but in recent years, new devices directly acting at the brain level have been proposed. In order to design the next-generation of neuroprosthetic devices for brain repair, we foresee the increasing exploitation of closed-loop systems enabled with neuromorphic elements due to their intrinsic energy efficiency, their capability to perform real-time data processing, and of mimicking neurobiological computation for an improved synergy between the technological and biological counterparts. In this manuscript, after providing definitions of key concepts, we reviewed the first exploitation of a real-time hardware neuromorphic prosthesis to restore the bidirectional communication between two neuronal populations in vitro. Starting from that ‘case-study’, we provide perspectives on the technological improvements for real-time interfacing and processing of neural signals and their potential usage for novel in vitro and in vivo experimental designs. The development of innovative neuroprosthetics for translational purposes is also presented and discussed. In our understanding, the pursuit of neuromorphic-based closed-loop neuroprostheses may spur the development of novel powerful technologies, such as ‘brain-prostheses’, capable of rewiring and/or substituting the injured nervous system. Full article

As the amount of data has grown exponentially with the advent of artificial intelligence and the Internet of Things, computing systems with high energy efficiency, high scalability, and high processing speed are urgently required. Unlike traditional digital computing, which suffers from the von Neumann bottleneck, brain-inspired computing can provide efficient, parallel, and low-power computation based on analog changes in synaptic connections between neurons. Synapse nodes in brain-inspired computing have been typically implemented with dozens of silicon transistors, which is an energy-intensive and non-scalable approach. Ion-movement-based synaptic devices for brain-inspired computing have attracted increasing attention for mimicking the performance of the biological synapse in the human brain due to their low area and low energy costs. This paper discusses the recent development of ion-movement-based synaptic devices for hardware implementation of brain-inspired computing and their principles of operation. From the perspective of the device-level requirements for brain-inspired computing, we address the advantages, challenges, and future prospects associated with different types of ion-movement-based synaptic devices. Full article

Information processing in the brain takes place in a dense network of neurons connected through synapses. The collaborative work between these two components (Synapses and Neurons) allows for basic brain functions such as learning and memorization. The so-called von Neumann bottleneck, which limits the information processing capability of conventional systems, can be overcome by the efficient emulation of these computational concepts. To this end, mimicking the neuronal architectures with silicon-based circuits, on which neuromorphic engineering is based, is accompanied by the development of new devices with neuromorphic functionalities. We shall study different memristor cellular nonlinear networks models. The rigorous mathematical analysis will be presented based on local activity theory, and the edge of chaos domain will be determined in the models under consideration. Simulations of these models working on the edge of chaos will show the generation of static and dynamic patterns. Full article

In a hardware-based neuromorphic computation system, using emerging nonvolatile memory devices as artificial synapses, which have an inelastic memory characteristic, has attracted considerable interest. In contrast, the elastic artificial neurons have received much less attention. An ideal material system that is suitable for mimicking biological neurons is the one with volatile (or mono-stable) resistive change property. Vanadium dioxide (VO₂) is a well-known material that exhibits an abrupt and volatile insulator-to-metal transition property. In this work, we experimentally demonstrate that pulse-driven two-terminal VO₂ devices behave in a leaky integrate-and-fire (LIF) manner, and they elastically relax back to their initial value after firing, thus, mimicking the behavior of biological neurons. The VO₂ device with a channel length of 20 µm can be driven to fire by a single long-duration pulse (>83 µs) or multiple short-duration pulses. We further model the VO₂ devices as resistive networks based on their granular domain structure, with resistivities corresponding to the insulator or metallic states. Simulation results confirm that the volatile resistive transition under voltage pulse driving is caused by the formation of a metallic filament in an avalanche-like process, while this volatile metallic filament will relax back to the insulating state at the end of driving pulses. The simulation offers a microscopic view of the dynamic and abrupt filament formation process to explain the experimentally observed LIF behavior. These results suggest that VO₂ insulator–metal transition could be exploited for artificial neurons. Full article

Hydrogels are the ideal materials in the development of implanted bioactive neural interfaces because of the nerve tissue-mimicked physical and biological properties that can enhance neural interfacing compatibility. However, the integration of hydrogels and rigid/dehydrated electronic microstructure is challenging due to the non-reliable interfacial bonding, whereas hydrogels are not compatible with most conditions required for the micromachined fabrication process. Herein, we propose a new enzyme-mediated transfer printing process to design an adhesive biological hydrogel neural interface. The donor substrate was fabricated via photo-crosslinking of gelatin methacryloyl (GelMA) containing various conductive nanoparticles (NPs), including Ag nanowires (NWs), Pt NWs, and PEDOT:PSS, to form a stretchable conductive bioelectrode, called NP-doped GelMA. On the other hand, a receiver substrate composed of microbial transglutaminase-incorporated gelatin (mTG-Gln) enabled simultaneous temporally controlled gelation and covalent bond-enhanced adhesion to achieve one-step transfer printing of the prefabricated NP-doped GelMA features. The integrated hydrogel microelectrode arrays (MEA) were adhesive, and mechanically/structurally bio-compliant with stable conductivity. The devices were structurally stable in moisture to support the growth of neuronal cells. Despite that the introduction of AgNW and PEDOT:PSS NPs in the hydrogels needed further study to avoid cell toxicity, the PtNW-doped GelMA exhibited a comparable live cell density. This Gln-based MEA is expected to be the next-generation bioactive neural interface. Full article

Large device variation is a fundamental challenge for resistive random access memory (RRAM) array circuit. Improved device-to-device distributions of set and reset voltages in a SiN_x RRAM device is realized via arsenic ion (As⁺) implantation. Besides, the As⁺-implanted SiN_x RRAM device exhibits much tighter cycle-to-cycle distribution than the nonimplanted device. The As⁺-implanted SiN_x device further exhibits excellent performance, which shows high stability and a large 1.73 × 10³ resistance window at 85 °C retention for 10⁴ s, and a large 10³ resistance window after 10⁵ cycles of the pulsed endurance test. The current–voltage characteristics of high- and low-resistance states were both analyzed as space-charge-limited conduction mechanism. From the simulated defect distribution in the SiN_x layer, a microscopic model was established, and the formation and rupture of defect-conductive paths were proposed for the resistance switching behavior. Therefore, the reason for such high device performance can be attributed to the sufficient defects created by As⁺ implantation that leads to low forming and operation power. Full article

In this work, we explore the use of the resistive random access memory (RRAM) device as a synapse for mimicking the trained weights linking neurons in a deep learning neural network (DNN) (AlexNet). The RRAM devices were fabricated in-house and subjected to 1000 bipolar read-write cycles to measure the resistances recorded for Logic-0 and Logic-1 (we demonstrate the feasibility of achieving eight discrete resistance states in the same device depending on the RESET stop voltage). DNN simulations have been performed to compare the relative error between the output of AlexNet Layer 1 (Convolution) implemented with the standard backpropagation (BP) algorithm trained weights versus the weights that are encoded using the measured resistance distributions from RRAM. The IMAGENET dataset is used for classification purpose here. We focus only on the Layer 1 weights in the AlexNet framework with 11 × 11 × 96 filters values coded into a binary floating point and substituted with the RRAM resistance values corresponding to Logic-0 and Logic-1. The impact of variability in the resistance states of RRAM for the low and high resistance states on the accuracy of image classification is studied by formulating a look-up table (LUT) for the RRAM (from measured I-V data) and comparing the convolution computation output of AlexNet Layer 1 with the standard outputs from the BP-based pre-trained weights. This is one of the first studies dedicated to exploring the impact of RRAM device resistance variability on the prediction accuracy of a convolutional neural network (CNN) on an AlexNet platform through a framework that requires limited actual device switching test data. Full article

Optic neuropathies, such as glaucoma and Leber’s hereditary optic neuropathy (LHON) lead to retinal ganglion cell (RGC) loss and therefore motivate the application of transplantation technique into disease therapy. However, it is a challenge to direct the transplanted optic nerve axons to the correct location of the retina. The use of appropriate scaffold can promote the proper axon growth. Recently, biocompatible materials have been integrated into the medical field, such as tissue engineering and reconstruction of damaged tissues or organs. We, herein, utilized nano-imprinting to create a scaffold mimicking the in vitro tissue microarchitecture, and guiding the axonal growth and orientation of the RGCs. We observed that the robust, long, and organized axons of human induced pluripotent stem cell (iPSC)-derived RGCs projected axially along the scaffold grooves. The RGCs grown on the scaffold expressed the specific neuronal biomarkers indicating their proper functionality. Thus, based on our in vitro culture system, this device can be useful for the neurophysiological analysis and transplantation for ophthalmic neuropathy treatment. Full article