Advances in memristor based artificial neuron fabrication-materials,models,and applications

Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and low energy consumption characteristics.Analogous to the working mechanism of human brain,the SNN system transmits information through the spiking action of neurons.Therefore,artificial neurons are critical building blocks for constructing SNN in hardware.Memristors are drawing growing attention due to low consumption,high speed,and nonlinearity characteristics,which are recently introduced to mimic the functions of biological neurons.Researchers have proposed multifarious memristive materials including organic materials,inorganic materials,or even two-dimensional materials.Taking advantage of the unique electrical behavior of these materials,several neuron models are successfully implemented,such as Hodgkin–Huxley model,leaky integrate-and-fire model and integrate-and-fire model.In this review,the recent reports of artificial neurons based on memristive devices are discussed.In addition,we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic devices.Finally,the future challenges and outlooks of memristor-based artificial neurons are discussed,and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected.

Advances in neuromorphic computing:Expanding horizons for AI development through novel artificial neurons and in-sensor computing

AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by the conventional computing hardware.In the post-Moore era,the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits(VLSIC)is challenging to meet the growing demand for AI computing power.To address the issue,technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture,and dealing with AI algorithms much more parallelly and energy efficiently.Inspired by the human neural network architecture,neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or devices.Although it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network(SNN),the development in this field has incubated promising technologies like in-sensor computing,which brings new opportunities for multidisciplinary research,including the field of optoelectronic materials and devices,artificial neural networks,and microelectronics integration technology.The vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive processing.This paper reviews firstly the architectures and algorithms of SNN,and artificial neuron devices supporting neuromorphic computing,then the recent progress of in-sensor computing vision chips,which all will promote the development of AI.

FK506 contributes to peripheral nerve regeneration by inhibiting neuroinflammatory responses and promoting neuron survival

FK506(Tacrolimus)is a systemic immunosuppressant approved by the U.S.Food and Drug Administration.FK506 has been shown to promote peripheral nerve regeneration,however,its precise mechanism of action and its pathways remain unclear.In this study,we established a rat model of sciatic nerve injury and found that FK506 improved the morphology of the injured sciatic nerve,increased the numbers of motor and sensory neurons,reduced inflammatory responses,markedly improved the conduction function of the injured nerve,and promoted motor function recovery.These findings suggest that FK506 promotes peripheral nerve structure recovery and functional regeneration by reducing the intensity of inflammation after neuronal injury and increasing the number of surviving neurons.

Inhibitory gamma-aminobutyric acidergic neurons in the anterior cingulate cortex participate in the comorbidity of pain and emotion

Pain is often comorbid with emotional disorders such as anxiety and depression.Hyperexcitability of the anterior cingulate cortex has been implicated in pain and pain-related negative emotions that arise from impairments in inhibitory gamma-aminobutyric acid neurotransmission.This review primarily aims to outline the main circuitry(including the input and output connectivity)of the anterior cingulate cortex and classification and functions of different gamma-aminobutyric acidergic neurons;it also describes the neurotransmitters/neuromodulators affecting these neurons,their intercommunication with other neurons,and their importance in mental comorbidities associated with chronic pain disorders.Improving understanding on their role in pain-related mental comorbidities may facilitate the development of more effective treatments for these conditions.However,the mechanisms that regulate gamma-aminobutyric acidergic systems remain elusive.It is also unclear as to whether the mechanisms are presynaptic or postsynaptic.Further exploration of the complexities of this system may reveal new pathways for research and drug development.

Many faces of neuronal activity manipulation in Drosophila

Animals exhibit complex responses to external and internal stimuli.The information is computed by interconnected neurons that express numerous ion channels,which modulate the neuronal membrane potential.How can neuronal activity orchestrate complex motor patterns or allow learning from previous experience?To answer such questions,we need the ability not only to record,but also to modulate neuronal activity in both space(e.g.,neuronal subsets)and time.

Single-cell pan-omics, environmental neurology, and artificial intelligence:the time for holistic brain health research

The brain,with its trillions of neural connections,different cellular types,and molecular complexities,presents a formidable challenge for researchers aiming to comprehend the multifaceted nature of neural health.As traditional methods have provided valuable insights,emerging technologies offer unprecedented opportunities to delve deeper into the underpinnings of brain function.In the everevolving landscape of neuroscience,the quest to unravel the mysteries of the human brain is bound to take a leap forward thanks to new technological improvements and bold interpretative frameworks.

Small molecule inhibitor DDQ-treated hippocampal neuronal cells show improved neurite outgrowth and synaptic branching

The process of neurite outgrowth and branching is a crucial aspect of neuronal development and regeneration.Axons and dendrites,sometimes referred to as neurites,are extensions of a neuron's cellular body that are used to start networks.Here we explored the effects of diethyl(3,4-dihydroxyphenethylamino)(quinolin-4-yl)methylphosphonate(DDQ)on neurite developmental features in HT22 neuronal cells.In this work,we examined the protective effects of DDQ on neuronal processes and synaptic outgrowth in differentiated HT22cells expressing mutant Tau(mTau)cDNA.To investigate DDQ chara cteristics,cell viability,biochemical,molecular,western blotting,and immunocytochemistry were used.Neurite outgrowth is evaluated through the segmentation and measurement of neural processes.These neural processes can be seen and measured with a fluorescence microscope by manually tracing and measuring the length of the neurite growth.These neuronal processes can be observed and quantified with a fluorescent microscope by manually tracing and measuring the length of the neuronal HT22.DDQ-treated mTau-HT22 cells(HT22 cells transfected with cDNA mutant Tau)were seen to display increased levels of synaptophysin,MAP-2,andβ-tubulin.Additionally,we confirmed and noted reduced levels of both total and p-Tau,as well as elevated levels of microtubule-associated protein 2,β-tubulin,synaptophysin,vesicular acetylcholine transporter,and the mitochondrial biogenesis protein-pe roxisome prolife rator-activated receptor-gamma coactivator-1α.In mTa u-expressed HT22 neurons,we observed DDQ enhanced the neurite characteristics and improved neurite development through increased synaptic outgrowth.Our findings conclude that mTa u-HT22(Alzheimer's disease)cells treated with DDQ have functional neurite developmental chara cteristics.The key finding is that,in mTa u-HT22 cells,DDQ preserves neuronal structure and may even enhance nerve development function with mTa u inhibition.

Induced pluripotent stem cell-related approaches to generate dopaminergic neurons for Parkinson's disease

The progressive loss of dopaminergic neurons in affected patient brains is one of the pathological features of Parkinson's disease,the second most common human neurodegenerative disease.Although the detailed pathogenesis accounting for dopaminergic neuron degeneration in Parkinson's disease is still unclear,the advancement of stem cell approaches has shown promise for Parkinson's disease research and therapy.The induced pluripotent stem cells have been commonly used to generate dopaminergic neurons,which has provided valuable insights to improve our understanding of Parkinson's disease pathogenesis and contributed to anti-Parkinson's disease therapies.The current review discusses the practical approaches and potential applications of induced pluripotent stem cell techniques for generating and differentiating dopaminergic neurons from induced pluripotent stem cells.The benefits of induced pluripotent stem cell-based research are highlighted.Various dopaminergic neuron differentiation protocols from induced pluripotent stem cells are compared.The emerging three-dimension-based brain organoid models compared with conventional two-dimensional cell culture are evaluated.Finally,limitations,challenges,and future directions of induced pluripotent stem cell–based approaches are analyzed and proposed,which will be significant to the future application of induced pluripotent stem cell-related techniques for Parkinson's disease.

NOX4 exacerbates Parkinson's disease pathology by promoting neuronal ferroptosis and neuroinflammation

Parkinson's disease is primarily caused by the loss of dopaminergic neurons in the substantia nigra compacta.Ferroptosis,a novel form of regulated cell death characterized by iron accumulation and lipid peroxidation,plays a vital role in the death of dopaminergic neurons.However,the molecular mechanisms underlying ferroptosis in dopaminergic neurons have not yet been completely elucidated.NADPH oxidase 4 is related to oxidative stress,however,whether it regulates dopaminergic neuronal ferroptosis remains unknown.The aim of this study was to determine whether NADPH oxidase 4 is involved in dopaminergic neuronal ferroptosis,and if so,by what mechanism.We found that the transcriptional regulator activating transcription factor 3 increased NADPH oxidase 4 expression in dopaminergic neurons and astrocytes in an 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine-induced Parkinson's disease model.NADPH oxidase 4 inhibition improved the behavioral impairments observed in the Parkinson's disease model animals and reduced the death of dopaminergic neurons.Moreover,NADPH oxidase 4 inhibition reduced lipid peroxidation and iron accumulation in the substantia nigra of the Parkinson's disease model animals.Mechanistically,we found that NADPH oxidase 4 interacted with activated protein kinase Cαto prevent ferroptosis of dopaminergic neurons.Furthermore,by lowering the astrocytic lipocalin-2 expression,NADPH oxidase 4 inhibition reduced 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine-induced neuroinflammation.These findings demonstrate that NADPH oxidase 4 promotes ferroptosis of dopaminergic neurons and neuroinflammation,which contribute to dopaminergic neuron death,suggesting that NADPH oxidase 4 is a possible therapeutic target for Parkinson's disease.

Inhibition of the NLRP3 inflammasome attenuates spiral ganglion neuron degeneration in aminoglycoside-induced hearing loss

Aminoglycosides are a widely used class of antibacterials renowned for their effectiveness and broad antimicrobial spectrum.However,their use leads to irreversible hearing damage by causing apoptosis of hair cells as their direct target.In addition,the hearing damage caused by aminoglycosides involves damage of spiral ganglion neurons upon exposure.To investigate the mechanisms underlying spiral ganglion neuron degeneration induced by aminoglycosides,we used a C57BL/6J mouse model treated with kanamycin.We found that the mice exhibited auditory deficits following the acute loss of outer hair cells.Spiral ganglion neurons displayed hallmarks of pyroptosis and exhibited progressive degeneration over time.Transcriptomic profiling of these neurons showed significant upregulation of genes associated with inflammation and immune response,particularly those related to the NLRP3 inflammasome.Activation of the canonical pyroptotic pathway in spiral ganglion neurons was observed,accompanied by infiltration of macrophages and the release of proinflammatory cytokines.Pharmacological intervention targeting NLRP3 using Mcc950 and genetic intervention using NLRP3 knockout ameliorated spiral ganglion neuron degeneration in the injury model.These findings suggest that NLRP3 inflammasome-mediated pyroptosis plays a role in aminoglycoside-induced spiral ganglion neuron degeneration.Inhibition of this pathway may offer a potential therapeutic strategy for treating sensorineural hearing loss by reducing spiral ganglion neuron degeneration.

Recognition and quality mapping of traditional herbal drugs:way forward towards artificial intelligence

The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for identifying and mapping the quality of these herbal medicines.This article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal drugs.It focuses on feature extraction methods,image processing techniques,and the preparation of herbal images for compatibility with machine learning models.The article discusses commonly used image processing tools such as normalization,slicing,cropping,and augmentation to prepare images for artificial intelligence-based models.It also provides an overview of global herbal image databases and the models employed for herbal plant/drug identification.Readers will gain a comprehensive understanding of the potential application of various machine learning models,including artificial neural networks and convolutional neural networks.The article delves into suitable validation parameters like true positive rates,accuracy,precision,and more for the development of artificial intelligence-based identification and authentication techniques for herbal drugs.This article offers valuable insights and a conclusive platform for the further exploration of artificial intelligence in the field of herbal drugs,paving the way for smarter identification and authentication methods.

FUBP3 mediates the amyloid-β-induced neuronal NLRP3 expression

Alzheimer's disease is characterized by deposition of amyloid-β,which forms extracellular neuritic plaques,and accumulation of hyperphosphorylated tau,which aggregates to form intraneuronal neurofibrillary tangles,in the brain.The NLRP3 inflammasome may play a role in the transition from amyloid-βdeposition to tau phosphorylation and aggregation.Because NLRP3 is primarily found in brain microglia,and tau is predominantly located in neurons,it has been suggested that NLRP3 expressed by microglia indirectly triggers tau phosphorylation by upregulating the expression of pro-inflammatory cytokines.Here,we found that neurons also express NLRP3 in vitro and in vivo,and that neuronal NLRP3 regulates tau phosphorylation.Using biochemical methods,we mapped the minimal NLRP3 promoter and identified FUBP3 as a transcription factor regulating NLRP3 expression in neurons.In primary neurons and the neuroblastoma cell line Neuro2A,FUBP3 is required for endogenous NLRP3 expression and tau phosphorylation only when amyloid-βis present.In the brains of aged wild-type mice and a mouse model of Alzheimer's disease,FUBP3 expression was markedly increased in cortical neurons.Transcriptome analysis suggested that FUBP3 plays a role in neuron-mediated immune responses.We also found that FUBP3 trimmed the 5′end of DNA fragments that it bound,implying that FUBP3 functions in stress-induced responses.These findings suggest that neuronal NLRP3 may be more directly involved in the amyloid-β-to–phospho-tau transition than microglial NLRP3,and that amyloid-βfundamentally alters the regulatory mechanism of NLRP3 expression in neurons.Given that FUBP3 was only expressed at low levels in young wild-type mice and was strongly upregulated in the brains of aged mice and Alzheimer's disease mice,FUBP3 could be a safe therapeutic target for preventing Alzheimer's disease progression.

Transforming growth factor-beta 1 enhances discharge activity of cortical neurons

Transforming growth factor-beta 1(TGF-β1)has been extensively studied for its pleiotropic effects on central nervous system diseases.The neuroprotective or neurotoxic effects of TGF-β1 in specific brain areas may depend on the pathological process and cell types involved.Voltage-gated sodium channels(VGSCs)are essential ion channels for the generation of action potentials in neurons,and are involved in various neuroexcitation-related diseases.However,the effects of TGF-β1 on the functional properties of VGSCs and firing properties in cortical neurons remain unclear.In this study,we investigated the effects of TGF-β1 on VGSC function and firing properties in primary cortical neurons from mice.We found that TGF-β1 increased VGSC current density in a dose-and time-dependent manner,which was attributable to the upregulation of Nav1.3 expression.Increased VGSC current density and Nav1.3 expression were significantly abolished by preincubation with inhibitors of mitogen-activated protein kinase kinase(PD98059),p38 mitogen-activated protein kinase(SB203580),and Jun NH2-terminal kinase 1/2 inhibitor(SP600125).Interestingly,TGF-β1 significantly increased the firing threshold of action potentials but did not change their firing rate in cortical neurons.These findings suggest that TGF-β1 can increase Nav1.3 expression through activation of the ERK1/2-JNK-MAPK pathway,which leads to a decrease in the firing threshold of action potentials in cortical neurons under pathological conditions.Thus,this contributes to the occurrence and progression of neuroexcitatory-related diseases of the central nervous system.

Harnessing artificial intelligence for identifying conflicts of interest in research

This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recognition,and natural language processing techniques,AI offers innovative solutions for enhancing transparency and integrity in research.This editorial discusses how AI can automatically detect COIs,integrate data from various sources,and streamline reporting processes,thereby maintaining the credibility of scientific findings.

The burden of upper motor neuron involvement is correlated with the bilateral limb involvement interval in patients with amyotrophic lateral sclerosis:a retrospective observational study

Amyotrophic lateral sclerosis is a rare neurodegenerative disease characterized by the involvement of both upper and lower motor neurons.Early bilateral limb involvement significantly affects patients'daily lives and may lead them to be confined to bed.However,the effect of upper and lower motor neuron impairment and other risk factors on bilateral limb involvement is unclear.To address this issue,we retrospectively collected data from 586 amyotrophic lateral sclerosis patients with limb onset diagnosed at Peking University Third Hospital between January 2020 and May 2022.A univariate analysis revealed no significant differences in the time intervals of spread in different directions between individuals with upper motor neuron-dominant amyotrophic lateral sclerosis and those with classic amyotrophic lateral sclerosis.We used causal directed acyclic graphs for risk factor determination and Cox proportional hazards models to investigate the association between the duration of bilateral limb involvement and clinical baseline characteristics in amyotrophic lateral sclerosis patients.Multiple factor analyses revealed that higher upper motor neuron scores(hazard ratio[HR]=1.05,95%confidence interval[CI]=1.01–1.09,P=0.018),onset in the left limb(HR=0.72,95%CI=0.58–0.89,P=0.002),and a horizontal pattern of progression(HR=0.46,95%CI=0.37–0.58,P<0.001)were risk factors for a shorter interval until bilateral limb involvement.The results demonstrated that a greater degree of upper motor neuron involvement might cause contralateral limb involvement to progress more quickly in limb-onset amyotrophic lateral sclerosis patients.These findings may improve the management of amyotrophic lateral sclerosis patients with limb onset and the prediction of patient prognosis.

High-performance artificial neurons based on Ag/MXene/GST/Pt threshold switching memristors

Threshold switching(TS) memristors can be used as artificial neurons in neuromorphic systems due to their continuous conductance modulation, scalable and energy-efficient properties. In this paper, we propose a low power artificial neuron based on the Ag/MXene/GST/Pt device with excellent TS characteristics, including a low set voltage(0.38 V)and current(200 nA), an extremely steep slope(< 0.1 m V/dec), and a relatively large off/on ratio(> 10^(3)). Besides, the characteristics of integrate and fire neurons that are indispensable for spiking neural networks have been experimentally demonstrated. Finally, its memristive mechanism is interpreted through the first-principles calculation depending on the electrochemical metallization effect.

A Novel Artificial Neuron-Like Gas Sensor Constructed from CuS Quantum Dots/Bi2S3 Nanosheets

Real-time rapid detection of toxic gases at room temperature is particularly important for public health and environmental monitoring.Gas sensors based on conventional bulk materials often suffer from their poor surface-sensitive sites,leading to a very low gas adsorption ability.Moreover,the charge transportation efficiency is usually inhibited by the low defect density of surface-sensitive area than that in the interior.In this work,a gas sensing structure model based on CuS quantum dots/Bi_(2)S_(3) nanosheets(CuS QDs/Bi_(2)S_(3) NSs)inspired by artificial neuron network is constructed.Simulation analysis by density functional calculation revealed that CuS QDs and Bi_(2)S_(3) NSs can be used as the main adsorption sites and charge transport pathways,respectively.Thus,the high-sensitivity sensing of NO_(2) can be realized by designing the artificial neuron-like sensor.The experimental results showed that the CuS QDs with a size of about 8 nm are highly adsorbable,which can enhance the NO_(2) sensitivity due to the rich sensitive sites and quantum size effect.The Bi_(2)S_(3) NSs can be used as a charge transfer network channel to achieve efficient charge collection and transmission.The neuron-like sensor that simulates biological smell shows a significantly enhanced response value(3.4),excellent responsiveness(18 s)and recovery rate(338 s),low theoretical detection limit of 78 ppb,and excellent selectivity for NO_(2).Furthermore,the developed wearable device can also realize the visual detection of NO2 through real-time signal changes.

Artificial neuron and synapse in spintronics devices

Neuromorphic computing is the development of computingschemes inspired by the processing of information in thebrain, which can execute complex tasks very efficiently usingan architecture that is completely different from that of semiconductorchips. Recently, researchers from Tohoku Universityhave realized an artificial neuron and synapse in spintronicsdevices, which are promising for future energy-efficientand adoptive computing systems, as they behave likethe spiking neural network in human brains.

A New Kind of Artificial Neuron Models

ＡＮｅｗＫｉｎｄｏｆＡｒｔｉｆｉｃｉａｌＮｅｕｒｏｎＭｏｄｅｌｓＦＥＮＧＹｉｎｇｉｕｎＲＡＮＱｉｗｅｎＭＡＯＷｅｎｇｅ（冯英浚）（冉启文）（毛文革）（Ｄｅｐｔ．ｏｆＭａｔｈｅｍａｔｉｃｓ，ＨａｒｂｉｎＩｎｓｔｉｔｕｔｅｏｆＴｅｃｈｎｏｌｏｇｙ，Ｈａｒ...

Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.