The cell biology of synaptic plasticity

An overview of extracellular field potentials: Different potentiation and measurable components, interpretations, and hippocampal synaptic activity models

The hippocampus and some other brain regions are critically involved in synaptic plasticity. Electrophysiological recordings using extracellular field potentials (EFPs) reveal diverse synaptic activity within the hippocampus, including input/output functions (reflecting neural excitability), paired-pulse responses (reflecting short-term plasticity), and long-term potentiation (reflecting long-term plasticity). EFP techniques offer various measurable components for assessing multiple neural functions. These include fEPSP slope, amplitude, and area under curve (AUC), as well as latency (fEPSP onset or peak after stimulation), width at half amplitude, fiber volley, decay time, time-course (fEPSP rise and decay time constants; tau), initial slope/initial area and -/late area derived from a fEPSP waveform sample. Each of these parameters is separately evaluated and provides distinct electrophysiological interpretations. Despite the rich data offered by EFP techniques, many studies adopt a limited approach, focusing solely on fEPSP slope, amplitude, and occasionally AUC, thereby neglecting the potential insights provided by other parameters. Given the inherent variability of fEPSP components within a single recording and timeframe, a comprehensive analysis of synaptic activity within a specific hippocampal region is necessary for obtaining the full spectrum of fEPSP-related data. Researchers should consider the potential influence of additional factors contributing to the variability of synaptic activity magnitude. A detailed analysis considering different parts of extracellular fEPSP recordings and their properties is crucial for a deeper understanding of synaptic activity changes within the brain. Therefore, this review aims to provide a comprehensive overview of diverse forms of hippocampal synaptic activity, measurable components of EFP recordings, and their corresponding interpretations.

Neural stem cell-derived exosomes improve neurite outgrowth and cognitive function through transferring miR-132-3p

BACKGROUND/AIMS

Vascular dementia (VD) is accompanied by severe neuronal damage. Exosomal microRNAs (miRs) have been implicated in the neuroprotective effect of neural stem cells (NSCs), and miR-132-3p is a proneurogenic miR. In this study, we aimed to explore the role and underlying mechanisms of miR-132-3p-enriched NSC-EXs in VD-induced neuronal damage and synaptic impairment.

METHODS

NSC-EXs, NSC-EXs enriched with miR-132-3p (NSC-EXsmiR-132-3p), and NSC-EXs deficient in miR-132-3p (NSC-EXssimiR-132-3p) were cocultured with oxygen- and glucose-deprived (OGD)-injured neurons or administered to VD mice. Bioinformatic analyses and luciferase assays were used to determine the target genes of miR-132-3p.

RESULTS

The levels of NSC-EXs and their associated miR-132-3p were markedly decreased in the hippocampi of VD mice. Compared with NSC-EXs, the infusion of NSC-EXsmiR-132-3p was more effective at increasing the miR-132-3p level, neuron number, dendritic spine density and cognitive function and decreasing neuronal ROS production and apoptosis, whereas NSC-EXssimiR-132-3p treatment resulted in attenuated effects in comparison with those of NSC-EXs. In OGD-treated neurons, incubation with NSC-EXsmiR-132-3p increased neurite outgrowth and decreased neuronal ROS production and apoptosis. Moreover, through bioinformatic analysis and cell transfection, we confirmed that NSC-EXsmiR-132-3p promoted neurite outgrowth by targeting RASA1 and increasing the expression of downstream Ras and the phosphorylation of ERK1/2.

CONCLUSIONS

Our findings indicate that miR-132-3p enrichment promotes the efficacy of NSC-EXs in treating VD-induced neuronal damage and synaptic impairment via the inhibition of RASA1 and the activation of the downstream Ras/ERK1/2 signaling pathway.

Planar p-n Junction Engineering toward Reconfigurable Organic Synaptic Transistors for High-Accuracy Neuromorphic Recognition

Synaptic transistors are pivotal for hardware-level neuromorphic computing. However, the lack of switching behavior diversity has limited the implementation of advanced computing tasks, which are constrained by traditional interfacial or uncontrollable materials engineering. Here, a universal planar p-n junction structure is devised, with rational alignment of energy levels between crosslinkable p-type poly(indacenodithiophene-alt-benzothiadizole)-based conjugated polymer with hydroxyl groups at the ends of its side chains (OH-IDTBT-10%) and different n-type conjugated polymers, fabricated through efficient solution processing. This structure enables reconfigurable switching of p-type and n-type carrier transport by modifying the transistor architecture, along with significant non-volatile memory and synaptic plasticity. By strategically adjusting crosslinkers, a large memory window up to 48.5 V is achieved, sustained performance over 500 cycles, and a diverse array of synaptic behaviors modulated by electrical pulses. The underlying mechanism involves quantum well-like structures and discrete physical charge traps at the bilayer interface. The versatility of the strategy is proven across different n-type polymer systems. An artificial neural network (ANN) constructed by these devices affords a remarkably high facial recognition accuracy of 97.58% using the Yale Face Database with minimized training epochs of 200. This design provides an opportunity for high performance hardware with diverse synaptic behaviors in advanced neuromorphic computing.

Neuromorphic Phototransistor with Biochemical Reconfigurability

Neuromorphic transistors with biochemical perception and reconfigurability are crucial for seamless human-robot interaction. However, existing hardware is limited by solid-state operation with electronic charge dynamics. Here, we present such a neuromorphic organic photoelectrochemical transistor (OPECT), in which two different biomolecules can be recognized and transduced into either excitatory or inhibitory signals, exhibiting bioswitchable positive and negative photoconductivity. By adjusting the biomolecular ratios, it further enables dynamic conversion between excitatory and inhibitory behaviors, as well as between paired-pulse facilitation and depression. By constructing a closed-loop neuromorphic biosensing-feedback system, a reconfigurable OPECT nerve is further developed to simulate the chemical-mediated dilation and contraction of a human pupil.

Unravelling the Brain Resilience Following Stroke: From injury to rewiring of the brain through pathway activation, drug targets, and therapeutic interventions

Synaptic plasticity is a neuron's intrinsic ability to make new connections throughout life. The morphology and function of synapses are highly susceptible to any pathological condition. Ischemic stroke is a cerebrovascular event that affects various brain regions, resulting in the loss of neural networks. Stroke can alter both structural and functional plasticity of synapses, leading to long-term functional disability. Upon ischemic insult, numerous glutamate-mediated synaptic destruction pathways and glial-mediated phagocytic activity are triggered, resulting in excessive synapse loss, altering synaptic plasticity. The conventional stroke therapies to improve synaptic plasticity are still limited and ineffectual, leading to sub-optimal recovery in patients. Therefore, promoting synaptic plasticity to ameliorate sensory-motor function may be a promising strategy for long-term recovery in stroke patients. Here, we review the involvement of different molecular pathways of glutamate and glia-mediated synapse loss, current pharmacological targets, and the emerging novel approaches to improve synaptic plasticity and sensory-motor impairment post-stroke.

Ion-Electron Interactions in 2D Nanomaterials-Based Artificial Synapses for Neuromorphic Applications

With the increasing limitations of conventional computing techniques, particularly the von Neumann bottleneck, the brain's seamless integration of memory and processing through synapses offers a valuable model for technological innovation. Inspired by biological synapse facilitating adaptive, low-power computation by modulating signal transmission via ionic conduction, iontronic synaptic devices have emerged as one of the most promising candidates for neuromorphic computing. Meanwhile, the atomic-scale thickness and tunable electronic properties of van der Waals two-dimensional (2D) materials enable the possibility of designing highly integrated, energy-efficient devices that closely replicate synaptic plasticity. This review comprehensively analyzes advancements in iontronic synaptic devices based on 2D materials, focusing on electron-ion interactions in both iontronic transistors and memristors. The challenges of material stability, scalability, and device integration are evaluated, along with potential solutions and future research directions. By highlighting these developments, this review offers insights into the potential of 2D materials in advancing neuromorphic systems.

Sub-Millisecond and Energy-Efficient Electrochemical Synaptic Transistors with a Partially Reduced Graphene Oxide Channel

Designed artificial synaptic transistors, which emulate the functions of biological synapses, are intended to achieve information processing and computation, showcasing their promise in advancing artificial intelligence. Herein, we propose a synaptic transistor composed of a partially reduced graphene oxide (prGO) channel and a Nafion electrolyte, operating based on electrochemical reactions of the prGO channel, which are assisted by protons through the Nafion electrolyte. After electrical reduction of a pristine GO channel to the prGO channel by sweeping the drain voltage, the transistor exhibits over 200 distinct conductance states under applications of short gate voltage pulses down to 500 μs width, giving rise to a low energy consumption of 10-50 pJ per gate pulse. Using highly linear and symmetric long-term potentiation and depression characteristics, an image recognition accuracy using an artificial neural network based on a two-layer perceptron model is calculated to be 90%. If gate current pulses are used, the image recognition accuracy further increases to 94%, because of the improved linearity and symmetry of the conductance change. The transistor also exhibits short-term plasticity, such as paired-pulse facilitation and spike-timing-dependent plasticity, with time ranges of less than a few tens of milliseconds. These superior synaptic properties of the Nafion/prGO transistors will offer a remarkable paradigm for the development of neuromorphic computation architectures.

Flexible Tunable-Plasticity Synaptic Transistors for Mimicking Dynamic Cognition and Reservoir Computing

Inspired by biological systems, neuromorphic computing can process extensive data and complex tasks more efficiently than traditional architectures. Artificial synaptic devices, serving as fundamental components in neuromorphic computing, needto closely mimic synaptic characteristics and construct neural network computing systems. However, most existing multifunctional synapse devices are structurally complex and lack tunability, making them unsuitable for building smarter computing systems. In this work, a flexible tunable-plasticity synaptic transistor (TST) is realized with memory modulation and neuromorphic computing capabilities by using indium gallium zinc oxide as channel and a hybrid layer of polyimide and Al2O3 as dielectric. The TST exhibits a novel transition from short-term plasticity to long-term one by adjusting stimulus amplitude, mirroring dynamic human memory and forgetting behaviors across various scenarios. A neural network system with low non-linearity and a wide range of conductance variations is constructed, and it demonstrates a 94.1% recognition rate on classical datasets. A reservoir computing system for 4-bit coding is also developed, which significantly reduces computational complexity and network size without sacrificing recognition accuracy. The devices and the system work as the foundation of more intelligent and more efficient computing systems.

Uncovering synaptic and cellular nanoarchitecture of brain tissue via seamless in situ trimming and milling for cryo-electron tomography

Cell-cell communication underlies all emergent properties of the brain, including cognition, learning and memory. The physical basis for these communications is the synapse, a multi-component structure requiring coordinated interactions between diverse cell types. However, many aspects of three-dimensional (3D) synaptic organization remain poorly understood. Here, we developed an approach, seamless in situ trimming and milling (SISTM), to reliably fabricate sufficiently thin lamellae for mapping of the 3D nanoarchitecture of synapses in mouse, monkey and human brain tissue under near-native conditions via cryo-electron tomography (cryo-ET). We validated SISTM in a mouse model of Huntington's disease, demonstrating distinct 3D alterations to synaptic vesicles and mitochondria. By successfully applying SISTM to macaque brain, we described the 3D architecture of a tripartite synapse within the cortex. Subtomogram averaging (STA) enabled spatial mapping of astrocyte-neuron contacts within the tripartite synapse, revealing neurexin-neuroligin complexes as potential constituents that tether the two cell types. Finally, we showed that the defining features of synaptic nanoarchitecture were conserved across species and evident in human brain tissue obtained postmortem. Combining SISTM with cryo-ET and STA is a starting point for a new understanding of brain organization, disease-induced structural alterations and the development of rational, structure-guided therapeutics.

A Reliable High-Performance Floating-Gate Transistor Based on ZrS2 Native Oxidation for Optoelectronic Synergistic Artificial Synapses

Floating-gate transistors (FGTs), considered the most promising structure among three-terminal van der Waals (vdW) synaptic transistors, possess superiorities in improved data retention, excellent endurance properties, and multibit storage capacity, thereby overcoming the von Neumann bottleneck in conventional computing architectures. However, the dielectric layer in FGT devices typically depends on atomic layer deposition or mechanically transferred insulators, posing several challenges in terms of device compatibility, manufacturing complexity, and performance degradation. Therefore, it is crucial to discover dielectrics compatible with two-dimensional (2D) materials for further simplifying FGT structures and achieving optimal performance. Here, we present a controllable and reliable oxidation process to convert the 2D semiconductor ZrS2 into its native oxide ZrOx and combine ZrOx/ZrS2 with the MoS2 channel to form MoS2/ZrOx/ZrS2 FGT, which exhibits a high on/off ratio of 107, a wide memory window of 101 V, a long retention time of 103 s, a large storage capacity of 7 bits, an excellent PPF index of 269.4%, and low power consumption of 5 pJ. Under photoelectric stimulation, the device stimulates various biological synapse behaviors, including associative memory function and retina-like adaptation. In particular, the device achieves information storage and erasure under solely optical stimulation, exhibiting high consistency with synaptic weight modulation in optogenetics and outstanding optoelectronic storage performance. These results suggest that our work provides a novel and effective approach for simplifying FGT structures and enhancing their performance, holding significant potential for application in next-generation multifunctional memory devices and systems.

Turning garbage into gold: Autophagy in synaptic function

Trillions of synapses in the human brain enable thought and behavior. Synaptic connections must be established and maintained, while retaining dynamic flexibility to respond to experiences. These processes require active remodeling of the synapse to control the composition and integrity of proteins and organelles. Macroautophagy (hereafter, autophagy) provides a mechanism to edit and prune the synaptic proteome. Canonically, autophagy has been viewed as a homeostatic process, which eliminates aged and damaged proteins to maintain neuronal survival. However, accumulating evidence suggests that autophagy also degrades specific cargoes in response to neuronal activity to impact neuronal transmission, excitability, and synaptic plasticity. Here, we will discuss the diverse roles, regulation, and mechanisms of neuronal autophagy in synaptic function and contributions from glial autophagy in these processes.

Soft Artificial Synapse Electronics

Soft electronics, known for their bendable, stretchable, and flexible properties, are revolutionizing fields such as biomedical sensing, consumer electronics, and robotics. A primary challenge in this domain is achieving low power consumption, often hampered by the limitations of the conventional von Neumann architecture. In response, the development of soft artificial synapses (SASs) has gained substantial attention. These synapses seek to replicate the signal transmission properties of biological synapses, offering an innovative solution to this challenge. This review explores the materials and device architectures integral to SAS fabrication, emphasizing flexibility and stability under mechanical deformation. Various architectures, including floating-gate dielectric, ferroelectric-gate dielectric, and electrolyte-gate dielectric, are analyzed for effective weight control in SASs. The utilization of organic and low-dimensional materials is highlighted, showcasing their plasticity and energy-efficient operation. Furthermore, the paper investigates the integration of functionality into SASs, particularly focusing on devices that autonomously sense external stimuli. Functionalized SASs, capable of recognizing optical, mechanical, chemical, olfactory, and auditory cues, demonstrate promising applications in computing and sensing. A detailed examination of photo-functionalized, tactile-functionalized, and chemoreception-functionalized SASs reveals their potential in image recognition, tactile sensing, and chemosensory applications, respectively. This study highlights that SASs and functionalized SAS devices hold transformative potential for bioelectronics and sensing for soft-robotics applications; however, further research is necessary to address scalability, long-time stability, and utilizing functionalized SASs for prosthetics and in vivo applications through clinical adoption. By providing a comprehensive overview, this paper contributes to the understanding of SASs, bridging research gaps and paving the way toward transformative developments in soft electronics, biomimicking and biointegrated synapse devices, and integrated systems.

Dual-Modal Memory Enabled by a Single Vertical N-Type Organic Artificial Synapse for Neuromorphic Computing

Complementary neural network circuits combining multifunctional high-performance p-type with n-type organic artificial synapses satisfy sophisticated applications such as image cognition and prosthesis control. However, implementing the dual-modal memory features that are both volatile and nonvolatile in a synaptic transistor is challenging. Herein, for the first time, we propose a single vertical n-type organic synaptic transistor (VNOST) with a novel polymeric organic mixed ionic-electronic conductor as the core channel material to achieve dual-modal synaptic learning/memory behaviors at different operating current densities via the formation of an electric double layer and the reversible ion doping. As a volatile synaptic device, the resulting VNOST demonstrated an unprecedented operating current density of MA cm-2. Meanwhile, it is capable of 150 analog states, symmetric conductance modulation, and good state retention (100 s) for a nonvolatile synapse. Importantly, the artificial neural networks (ANNs) for recognition accuracy of the handwritten digital data sets recognition rate up to 94% based on its nonvolatile feature. This study provides a promising platform for building organic neuromorphic network circuits in complex application scenarios where high-performing n-type organic synapse transistors with dual-mode memory characters are necessitated.

Sustained Depolarization Induces Gene Expression Pattern Changes Related to Synaptic Plasticity in a Human Cholinergic Cellular Model

Neuronal gene expression in the brain dynamically responds to synaptic activity. The interplay among synaptic activity, gene expression, and synaptic plasticity has crucial implications for understanding the pathophysiology of diseases such as Alzheimer's disease and epilepsy. These diseases are marked by synaptic dysfunction that affects the expression patterns of neuroprotective genes that are incompletely understood. In our study, we developed a cellular model of synaptic activity using human cholinergic neurons derived from SH-SY5Y cell differentiation. Depolarization induction modulates the expression of neurotrophic genes and synaptic markers, indicating a potential role in synaptic plasticity regulation. This hypothesis is further supported by the induction kinetics of various long non-coding RNAs, including primate-specific ones. Our experimental model showcases the utility of SH-SY5Y cells in elucidating the molecular mechanisms underlying synaptic plasticity in human cellular systems.

Non-ionotropic NMDAR signalling activates Panx1 to induce P2X4R-dependent long-term depression in the hippocampus

In recent years, evidence supporting non-ionotropic signalling by the NMDA receptor (niNMDAR) has emerged, including roles in long-term depression (LTD). Here, we investigated whether niNMDAR-pannexin-1 (Panx1) contributes to LTD at the CA3-CA1 hippocampal synapse. Using whole-cell, patch clamp electrophysiology in rat hippocampal slices, we show that a low-frequency stimulation (3 Hz) of the Schaffer collaterals produces LTD that is blocked by continuous but not transient application of the NMDAR competitive antagonist, MK-801. After transient MK-801, LTD involved pannexin-1 and sarcoma (Src) kinase. We show that pannexin-1 is not permeable to Ca2+, but probably releases ATP to induce LTD via P2X4 purinergic receptors because LTD after transient MK-801 application was prevented by 5-BDBD. Thus, we conclude that niNMDAR activation of Panx1 can link glutamatergic and purinergic pathways to produce LTD following low frequency synaptic stimulation when NMDARs are transiently inhibited. KEY POINTS: Differential effect of short-term D-APV and MK-801 application on long-term depression (LTD) suggests that the NMDA receptor (niNMDAR) contributes to later phases of synaptic depression. niNMDAR LTD involved sarcoma (Src) kinase and pannexin-1 (Panx1), which is a pathway previously identified to be active during excitotoxicity. Panx1 was not calcium permeable but may contribute to late phase LTD via ATP release. Panx1 blockers prevent LTD, and this was rescued with exogenous ATP application. Inhibition of LTD with 5-BDBD suggests the downstream involvement of postsynaptic P2X4 receptors.

Self-Powered Photonic Synapses with Rapid Optical Erasing Ability for Neuromorphic Visual Perception

Photonic synapses combining photosensitivity and synaptic function can efficiently perceive and memorize visual information, making them crucial for the development of artificial vision systems. However, the development of high-performance photonic synapses with low power consumption and rapid optical erasing ability remains challenging. Here, we propose a photon-modulated charging/discharging mechanism for self-powered photonic synapses. The current hysteresis enables the devices based on CsPbBr3/solvent/carbon nitride multilayer architecture to emulate synaptic behaviors, such as excitatory postsynaptic currents, paired-pulse facilitation, and long/short-term memory. Intriguingly, the unique radiation direction-dependent photocurrent endows the photonic synapses with the capability of optical writing and rapid optical erasing. Moreover, the photonic synapses exhibit exceptional performance in contrast enhancement and noise reduction owing to the notable synaptic plasticity. In simulations based on artificial neural network (ANN) algorithms, the pre-processing by our photonic synapses improves the recognition rate of handwritten digit from 11.4% (200 training epochs) to 85% (~60 training epochs). Furthermore, due to the excellent feature extraction and memory capability, an array based on the photonic synapses can imitate facial recognition of human retina without the assistance of ANN.

Metal-Organic Framework Nanofluidic Synapse

Chemical synapse completes the signaling through neurotransmitter-mediated ion flux, the emulation of which has been a long-standing obstacle in neuromorphic exploration. Here, we report metal-organic framework (MOF) nanofluidic synapses in which conjugated MOFs with abundant ionic storage sites underlie the ionic hysteresis and simultaneously serve as catalase mimetics that sensitively respond to neurotransmitter glutamate (Glu). Various neurosynaptic patterns with adaptable weights are realized via Glu-mediated chemical/ionic coupling. In particular, nonlinear Hebbian and anti-Hebbian learning in millisecond time ranges are achieved, akin to those of chemical synapses. Reversible biochemical in-memory encoding via enzymatic Glu clearance is also accomplished. Such results are prerequisites for highly bionic electrolytic computers.

Ellagic acid improves the symptoms of early-onset Alzheimer's disease: Behavioral and physiological correlates

Oryza sativa is a globally recognized staple food, rich in essential phyto-phenolic compounds such as γ-Oryzanol (OZ), Ferulic acid (FA), and Ellagic acid (EA). These phytochemicals are known for their potential to beneficially modulate molecular biochemistry. The present investigation aimed to evaluate the neuroprotective and cognitive enhancement effects of Oryza sativa phyto-phenolics in a model of early-onset Alzheimer's disease (EOAD) induced by Aβ (1-42) in animals. In-silico studies suggested that FA, OZ, and EA have target specificity for Aβ, with EA being further selected based on its potent in-vitro Aβ anti-aggregatory effects for exploring neurodegenerative conditions. The in-vivo experiments demonstrated that EA exerts therapeutic effects in Aβ-induced EOAD, modulating both biochemical and behavioral outcomes. EA treatment at two dose levels, EA70 and EA140 (70 μM and 140 μM, respectively, administered i.c.v.), significantly counteracted Aβ aggregation and modulated the Ca2⁺/Calpain/GSK-3β/CDK5 signaling pathways, exhibiting anti-tauopathy effects. Additionally, EA was shown to exert anti-inflammatory effects by preventing astroglial activation, modulating FAIM-L expression, and protecting against TNF-α-induced apoptotic signals. Moreover, the neuromodulatory effects of EA were attributed to the regulation of CREB levels, Dnm-1 expression, and synaptophysin levels, thereby enhancing LTP and synaptic plasticity. EA also induced beneficial cytological and behavioral changes, improving both long-term and short-term spatial memory as well as associative learning behavior in the animal model, which underscores its cognitive enhancement properties.

Volume electron microscopy reveals 3D synaptic nanoarchitecture in postmortem human prefrontal cortex

Synaptic function is directly reflected in quantifiable ultrastructural features using electron microscopy (EM) approaches. This coupling of synaptic function and ultrastructure suggests that in vivo synaptic function can be inferred from EM analysis of ex vivo human brain tissue. To investigate this, we employed focused ion beam-scanning electron microscopy (FIB-SEM), a volume EM (VEM) approach, to generate ultrafine-resolution, three-dimensional (3D) micrographic datasets of postmortem human dorsolateral prefrontal cortex (DLPFC), a region with cytoarchitectonic characteristics distinct to human brain. Synaptic, sub-synaptic, and organelle measures were highly consistent with findings from experimental models that are free from antemortem or postmortem effects. Further, 3D neuropil reconstruction revealed a unique, ultrastructurally-complex, spiny dendritic shaft that exhibited features characteristic of heightened synaptic communication, integration, and plasticity. Altogether, our findings provide critical proof-of-concept data demonstrating that ex vivo VEM analysis is an effective approach to infer in vivo synaptic functioning in human brain.

Low-Cost, High-Efficiency Aluminum Zinc Oxide Synaptic Transistors: Blue LED Stimulation for Enhanced Neuromorphic Computing Applications

Neuromorphic devices are electronic devices that mimic the information processing methods of neurons and synapses, enabling them to perform multiple tasks simultaneously with low power consumption and exhibit learning ability. However, their large-scale production and efficient operation remain a challenge. Herein, we fabricated an aluminum-doped zinc oxide (AZO) synaptic transistor via solution-based spin-coating. The transistor is characterized by low production costs and high performance. It demonstrates high responsiveness under UV laser illumination. In addition, it exhibits effective synaptic behaviors under blue LED illumination, indicating high-efficiency operation. The paired-pulse facilitation (PPF) index measured from optical stimulus modulation was 179.6%, indicating strong synaptic connectivity and effective neural communication and processing. Furthermore, by modulating the blue LED light pulse frequency, an excitatory postsynaptic current gain of 4.3 was achieved, demonstrating efficient neuromorphic functionality. This study shows that AZO synaptic transistors are promising candidates for artificial synaptic devices.

Synaptic Transistors Using Scalable Graphene Nanoribbons

Graphene has demonstrated potential for use in neuromorphic electronics due to its superior electrical properties. However, these devices are all based on graphene sheets without patterning, restricting its applications. Here, we demonstrate a graphene nanoribbon synaptic transistor (GNST), with the graphene nanoribbon (GNR) channels fabricated using an electro-hydrodynamically printed nanowire array as lithographic masks for scalable fabrication. The GNST shows tunable synaptic plasticity by spike duration, frequency, and number. Moreover, the device is energy-efficient and ambipolar and shows a regulated response by nanoribbon width. The characteristics of GNSTs are applicable to pattern recognition, showing an accuracy of 84.5%. The device is applicable to Pavlov's classical conditioning. This study reports the first synaptic transistor based on GNRs, providing new insights into future neuromorphic electronics.

A bioinspired tactile scanner for computer haptics

Computer haptics (CH) is about integration of tactile sensation and rendering in Metaverse. However, unlike computer vision (CV) where both hardware infrastructure and software programs are well developed, a generic tactile data capturing device that serves the same role as what a camera does for CV, is missing. Bioinspired by electrophysiological processes in human tactile somatosensory nervous system, here we propose a tactile scanner along with a neuromorphically-engineered system, in which a closed-loop tactile acquisition and rendering (re-creation) are preliminarily achieved. Based on the architecture of afferent nerves and intelligent functions of mechano-gating and leaky integrate-and-fire models, such a tactile scanner is designed and developed by using piezoelectric transducers as axon neurons and thin film transistor (TFT)-based neuromorphic circuits to mimic synaptic behaviours and neural functions. As an example, the neuron-like tactile information of surface textures is captured and further used to render the texture friction of a virtual surface for "recreating" a "true" feeling of touch.

Effect of internal and external chaotic stimuli on synchronization of piezoelectric auditory neurons in coupled time-delay systems

Hearing impairment is considered to be related to the damage of hair cells or synaptic terminals, which will cause varying degrees of hearing loss. Numerous studies have shown that cochlear implants can balance this damage. The human ear receives external acoustic signals mostly under complex conditions, and its biophysical mechanisms have important significance for reference in the design of cochlear implants. However, the relevant biophysical mechanisms have not yet been fully determined. Using the characteristics of special acoustoelectric conversion in piezoelectric ceramics, this paper integrates them into the traditional FitzHugh-Nagumo neuron circuit and proposes a comprehensive model with coupled auditory neurons. The model comprehensively considers the effects of synaptic coupling between neurons, information transmission delay, external noise stimulation, and internal chaotic current stimulation on the synchronization of membrane potential signals of two auditory neurons. The experimental results show that coupling strength, delay size, noise intensity, and chaotic current intensity all have a certain regulatory effect on synchronization stability. In particular, when auditory neurons are in a chaotic state, their impact on synchronization stability is sensitive. Numerical results provide a reference for exploring the biophysical mechanisms of auditory neurons. At the same time, we are committed to providing assistance in using sensors to monitor signals and repair hearing impairments.

Decoding Arc transcription: a live-cell study of stimulation patterns and transcriptional output

Activity-regulated cytoskeleton-associated protein (Arc) plays a crucial role in synaptic plasticity, a process integral to learning and memory. Arc transcription is induced within a few minutes of stimulation, making it a useful marker for neuronal activity. However, the specific neuronal activity patterns that initiate Arc transcription have remained elusive due to the inability to observe mRNA transcription in live cells in real time. Using a genetically encoded RNA indicator (GERI) mouse model that expresses endogenous Arc mRNA tagged with multiple GFPs, we investigated Arc transcriptional activity in response to various electrical field stimulation patterns. The GERI mouse model was generated by crossing the Arc-PBS knock-in mouse, engineered with binding sites in the 3' untranslated region (UTR) of Arc mRNA, and the transgenic mouse expressing the cognate binding protein fused to GFP. In dissociated hippocampal neurons, we found that the pattern of stimulation significantly affects Arc transcription. Specifically, theta-burst stimulation consisting of high-frequency (100 Hz) bursts delivered at 10 Hz frequency induced the highest rate of Arc transcription. Concurrently, the amplitudes of nuclear calcium transients also reached their peak with 10 Hz burst stimulation, indicating a correlation between calcium concentration and transcription. However, our dual-color single-cell imaging revealed that there were no significant differences in calcium amplitudes between Arc-positive and Arc-negative neurons upon 10 Hz burst stimulation, suggesting the involvement of other factors in the induction of Arc transcription. Our live-cell RNA imaging provides a deeper insight into the complex regulation of transcription by activity patterns and calcium signaling pathways.

Nanofluidic Ionic Memristors

Living organisms use ions and small molecules as information carriers to communicate with the external environment at ultralow power consumption. Inspired by biological systems, artificial ion-based devices have emerged in recent years to try to realize efficient information-processing paradigms. Nanofluidic ionic memristors, memory resistors based on confined fluidic systems whose internal ionic conductance states depend on the historical voltage, have attracted broad attention and are used as neuromorphic devices for computing. Despite their high exposure, nanofluidic ionic memristors are still in the initial stage. Therefore, systematic guidance for developing and reasonably designing ionic memristors is necessary. This review systematically summarizes the history, mechanisms, and potential applications of nanofluidic ionic memristors. The essential challenges in the field and the outlook for the future potential applications of nanofluidic ionic memristors are also discussed.

Flexible Organic Electrochemical Transistors for Energy-Efficient Neuromorphic Computing

Brain-inspired flexible neuromorphic devices are of great significance for next-generation high-efficiency wearable sensing and computing systems. In this paper, we propose a flexible organic electrochemical transistor using poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)- 3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor and poly(methyl methacrylate) (PMMA)/LiClO4 solid-state electrolyte as the gate dielectric layer. Under gate voltage modulation, an electric double layer (EDL) forms between the dielectric layer and the channel, allowing the device to operate at low voltages. Furthermore, by leveraging the double layer effect and electrochemical doping within the device, we successfully mimic various synaptic behaviors, including excitatory post-synaptic currents (EPSC), paired-pulse facilitation (PPF), high-pass filtering characteristics, transitions from short-term plasticity (STP) to long-term plasticity (LTP), and demonstrate its image recognition and storage capabilities in a 3 × 3 array. Importantly, the device's electrical performance remains stable even after bending, achieving ultra-low-power consumption of 2.08 fJ per synaptic event at -0.001 V. This research may contribute to the development of ultra-low-power neuromorphic computing, biomimetic robotics, and artificial intelligence.

The Roles of hnRNP Family in the Brain and Brain-Related Disorders

Heterogeneous nuclear ribonucleoproteins (hnRNPs) belong to a complex family of RNA-binding proteins that are essential to control alternative splicing, mRNA trafficking, synaptic plasticity, stress granule formation, cell cycle regulation, and axonal transport. Over the past decade, hnRNPs have been associated with different brain disorders such as Alzheimer's disease, multiple sclerosis, and schizophrenia. Given their essential role in maintaining cell function and integrity, it is not surprising that dysregulated hnRNP levels lead to neurological implications. This review aims to explore the primary functions of hnRNPs in neurons, oligodendrocytes, microglia, and astrocytes, and their roles in brain disorders. We also discuss proteomics and other technologies and their potential for studying and evaluating hnRNPs in brain disorders, including the discovery of new therapeutic targets and possible pharmacological interventions.

1-Selector 1-Memristor Configuration with Multifunctional a-IGZO Memristive Devices Fabricated at Room Temperature

Serving as neuromorphic hardware accelerators, memristors play a crucial role in large-scale neuromorphic computing. Herein, two-terminal memristors utilizing amorphous indium-gallium-zinc oxide (a-IGZO) are fabricated through room-temperature sputtering. The electrical characteristics of these memristors are effectively modulated by varying the oxygen flow during the deposition process. The optimized a-IGZO memristor, fabricated under 3 sccm oxygen flow, presents a 5 × 103 ratio between its high- and low-resistance states, which can be maintained over 1 × 104 s with minimal degradation. Meanwhile, desirable properties such as electroforming-free and self-compliance, crucial for low-energy consumption, are also obtained in the a-IGZO memristor. Moreover, analog conductance switching is observed, demonstrating an interface-type behavior, as evidenced by its device-size-dependent performance. The coexistence of negative differential resistance with analog switching is attributed to the migration of oxygen vacancies and the trapping/detrapping of charges. Furthermore, the device demonstrates optical storage capabilities by exploiting the optical properties of a-IGZO, which can stably operate for up to 50 sweep cycles. Various synaptic functions have been demonstrated, including paired-pulse facilitation and spike-timing-dependent plasticity. These functionalities contribute to a simulated recognition accuracy of 90% for handwritten digits. Importantly, a one-selector one-memristor (1S1M) architecture is successfully constructed at room temperature by integrating a-IGZO memristor on a TaOx-based selector. This architecture exhibits a 107 on/off ratio, demonstrating its potential to suppress sneak currents among adjacent units in a memristor crossbar.

The Wingless planar cell polarity pathway is essential for optimal activity-dependent synaptic plasticity

From fly to man, the Wingless (Wg)/Wnt signaling molecule is essential for both the stability and plasticity of the nervous system. The Drosophila neuromuscular junction (NMJ) has proven to be a useful system for deciphering the role of Wg in directing activity-dependent synaptic plasticity (ADSP), which, in the motoneuron, has been shown to be dependent on both the canonical and the noncanonical calcium Wg pathways. Here we show that the noncanonical planar cell polarity (PCP) pathway is an essential component of the Wg signaling system controlling plasticity at the motoneuron synapse. We present evidence that disturbing the PCP pathway leads to a perturbation in ADSP. We first show that a PCP-specific allele of disheveled (dsh) affects the de novo synaptic structures produced during ADSP. We then show that the Rho GTPases downstream of Dsh in the PCP pathway are also involved in regulating the morphological changes that take place after repeated stimulation. Finally, we show that Jun kinase is essential for this phenomenon, whereas we found no indication of the involvement of the transcription factor complex AP1 (Jun/Fos). This work shows the involvement of the neuronal PCP signaling pathway in supporting ADSP. Because we find that AP1 mutants can perform ADSP adequately, we hypothesize that, upon Wg activation, the Rho GTPases and Jun kinase are involved locally at the synapse, in instructing cytoskeletal dynamics responsible for the appearance of the morphological changes occurring during ADSP.

Oxide Ionic Neuro-Transistors for Bio-inspired Computing

Current computing systems rely on Boolean logic and von Neumann architecture, where computing cells are based on high-speed electron-conducting complementary metal-oxide-semiconductor (CMOS) transistors. In contrast, ions play an essential role in biological neural computing. Compared with CMOS units, the synapse/neuron computing speed is much lower, but the human brain performs much better in many tasks such as pattern recognition and decision-making. Recently, ionic dynamics in oxide electrolyte-gated transistors have attracted increasing attention in the field of neuromorphic computing, which is more similar to the computing modality in the biological brain. In this review article, we start with the introduction of some ionic processes in biological brain computing. Then, electrolyte-gated ionic transistors, especially oxide ionic transistors, are briefly introduced. Later, we review the state-of-the-art progress in oxide electrolyte-gated transistors for ionic neuromorphic computing including dynamic synaptic plasticity emulation, spatiotemporal information processing, and artificial sensory neuron function implementation. Finally, we will address the current challenges and offer recommendations along with potential research directions.

Ionic Diffusive Nanomemristors with Dendritic Competition and Cooperation Functions for Ultralow Voltage Neuromorphic Computing

Realization of dendric signal processing in the human brain is of great significance for spatiotemporal neuromorphic engineering. Here, we proposed an ionic dendrite device with multichannel communication, which could realize synaptic behaviors even under an ultralow action potential of 80 mV. The device not only could simulate one-to-one information transfer of axons but also achieve a many-to-one modulation mode of dendrites. By the adjustment of two presynapses, Pavlov's dog conditioning experiment was learned successfully. Furthermore, the device also could emulate the biological synaptic competition and synaptic cooperation phenomenon through the comodulation of three presynapses, which are crucial for artificial neural network (ANN) implementation. Finally, an ANN was further constructed to realize highly efficient and anti-interference recognition of fashion patterns. By introducing the cooperative device, synaptic weight updates could be improved for higher linearity and larger dynamic regulation range in neuromorphic computing, resulting in higher recognition accuracy and efficiency. Such an artificial dendric device has great application prospects in the processing of more complex information and the construction of an ANN system with more functions.

Divergent Learning-Related Transcriptional States of Cortical Glutamatergic Neurons

Experience-dependent gene expression reshapes neural circuits, permitting the learning of knowledge and skills. Most learning involves repetitive experiences during which neurons undergo multiple stages of functional and structural plasticity. Currently, the diversity of transcriptional responses underlying dynamic plasticity during repetition-based learning is poorly understood. To close this gap, we analyzed single-nucleus transcriptomes of L2/3 glutamatergic neurons of the primary motor cortex after 3 d motor skill training or home cage control in water-restricted male mice. "Train" and "control" neurons could be discriminated with high accuracy based on expression patterns of many genes, indicating that recent experience leaves a widespread transcriptional signature across L2/3 neurons. These discriminating genes exhibited divergent modes of coregulation, differentiating neurons into discrete clusters of transcriptional states. Several states showed gene expressions associated with activity-dependent plasticity. Some of these states were also prominent in the previously published reference, suggesting that they represent both spontaneous and task-related plasticity events. Markedly, however, two states were unique to our dataset. The first state, further enriched by motor training, showed gene expression suggestive of late-stage plasticity with repeated activation, which is suitable for expected emergent neuronal ensembles that stably retain motor learning. The second state, equally found in both train and control mice, showed elevated levels of metabolic pathways and norepinephrine sensitivity, suggesting a response to common experiences specific to our experimental conditions, such as water restriction or circadian rhythm. Together, we uncovered divergent transcriptional responses across L2/3 neurons, each potentially linked with distinct features of repetition-based motor learning such as plasticity, memory, and motivation.

The influence of synaptic plasticity on critical coupling estimates for neural populations

The presence or absence of synaptic plasticity can dramatically influence the collective behavior of populations of coupled neurons. In this work, we consider spike-timing dependent plasticity (STDP) and its resulting influence on phase cohesion in computational models of heterogeneous populations of conductance-based neurons. STDP allows for the influence of individual synapses to change over time, strengthening or weakening depending on the relative timing of the relevant action potentials. Using phase reduction techniques, we derive an upper bound on the critical coupling strength required to retain phase cohesion for a network of synaptically coupled, heterogeneous neurons with STDP. We find that including STDP can significantly alter phase cohesion as compared to a network with static synaptic connections. Analytical results are validated numerically. Our analysis highlights the importance of the relative ordering of action potentials emitted in a population of tonically firing neurons and demonstrates that order switching can degrade the synchronizing influence of coupling when STDP is considered.

Insights into synaptic functionality and resistive switching in lead iodide flexible memristor devices

Neuromorphic platforms are gaining popularity due to their superior efficiency, low power consumption, and adaptable parallel signal processing capabilities, overcoming the limitations of traditional von Neumann architecture. We conduct an in-depth investigation into the factors influencing the resistive switching mechanism in memristor devices utilizing lead iodide (PbI2). We establish correlations between device performance and morphological features, unveiling synaptic like behaviour of device making it suitable for range of flexible neuromorphic applications. Notably, a highly reliable unipolar switching mechanism is identified, exhibiting stability even under mechanical strain (with a bending radius of approximately 4 mm) and in high humidity environment (at 75% relative humidity) without the need for encapsulation. The investigation delves into the complex interplay of charge transport, ion migration and the active interface, elucidating the factors contributing to the remarkable resistive switching observed in PbI2-based memristors. The detailed findings highlight synaptic behaviors akin to the modulation of synaptic strengths, with an impressive potentiation and depression of 2 × 104 cycles, emphasizing the role of spike time-dependent plasticity (STDP). The flexible platform demonstrates exceptional performance, achieving a simulated accuracy rate of 95.06% in recognizing modified patterns from the National Institute of Standards and Technology (MNIST) dataset with just 30 training epochs. Ultimately, this research underscores the potential of PbI2-based flexible memristor devices as versatile component for neuromorphic computing. Moreover, it demonstrate the robustness of PbI2 memristors in terms of their resistive switching capabilities, showcasing resilience both mechanically and electrically. This underscores their potential in replicating synaptic functions for advanced information processing systems.

A hybrid transistor with transcriptionally controlled computation and plasticity

Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.

The Roadmap of 2D Materials and Devices Toward Chips

Due to the constraints imposed by physical effects and performance degradation, silicon-based chip technology is facing certain limitations in sustaining the advancement of Moore's law. Two-dimensional (2D) materials have emerged as highly promising candidates for the post-Moore era, offering significant potential in domains such as integrated circuits and next-generation computing. Here, in this review, the progress of 2D semiconductors in process engineering and various electronic applications are summarized. A careful introduction of material synthesis, transistor engineering focused on device configuration, dielectric engineering, contact engineering, and material integration are given first. Then 2D transistors for certain electronic applications including digital and analog circuits, heterogeneous integration chips, and sensing circuits are discussed. Moreover, several promising applications (artificial intelligence chips and quantum chips) based on specific mechanism devices are introduced. Finally, the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed, and potential development pathways or roadmaps are further speculated and outlooked.

Low-power and high-speed HfLaO-based FE-TFTs for artificial synapse and reconfigurable logic applications

Emulating the human nervous system to build next-generation computing architectures is considered a promising way to solve the von Neumann bottleneck. Transistors based on ferroelectric layers are strong contenders for the basic unit of artificial neural systems due to their advantages of high speed and low power consumption. In this work, the potential of Fe-TFTs integrating the HfLaO ferroelectric film and ultra-thin ITO channel for artificial synaptic devices is demonstrated for the first time. The Fe-TFTs can respond significantly to pulses as low as 14 ns with an energy consumption of 93.1 aJ, which is at the leading level for similar devices. In addition, Fe-TFTs exhibit essential synaptic functions and achieve a recognition rate of 93.2% for handwritten digits. Notably, a novel reconfigurable approach involving the combination of two types of electrical pulses to realize Boolean logic operations ("AND", "OR") within a single Fe-TFT has been introduced for the first time. The simulations of array-level operations further demonstrated the potential for parallel computing. These multifunctional Fe-TFTs reveal new hardware options for neuromorphic computing chips.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

Hippocampal synaptic failure is an early event in experimental parkinsonism with subtle cognitive deficit

Learning and memory mainly rely on correct synaptic function in the hippocampus and other brain regions. In Parkinson's disease, subtle cognitive deficits may even precede motor signs early in the disease. Hence, we set out to unravel the earliest hippocampal synaptic alterations associated with human α-synuclein overexpression prior to and soon after the appearance of cognitive deficits in a parkinsonism model. We bilaterally injected adeno-associated viral vectors encoding A53T-mutated human α-synuclein into the substantia nigra of rats, and evaluated them 1, 2, 4 and 16 weeks post-inoculation by immunohistochemistry and immunofluorescence to study degeneration and distribution of α-synuclein in the midbrain and hippocampus. The object location test was used to evaluate hippocampal-dependent memory. Sequential window acquisition of all theoretical mass spectrometry-based proteomics and fluorescence analysis of single-synapse long-term potentiation were used to study alterations to protein composition and plasticity in isolated hippocampal synapses. The effect of L-DOPA and pramipexole on long-term potentiation was also tested. Human α-synuclein was found within dopaminergic and glutamatergic neurons of the ventral tegmental area, and in dopaminergic, glutamatergic and GABAergic axon terminals in the hippocampus from 1 week post-inoculation, concomitant with mild dopaminergic degeneration in the ventral tegmental area. In the hippocampus, differential expression of proteins involved in synaptic vesicle cycling, neurotransmitter release and receptor trafficking, together with impaired long-term potentiation were the first events observed (1 week post-inoculation), preceding cognitive deficits (4 weeks post-inoculation). Later on, at 16 weeks post-inoculation, there was a deregulation of proteins involved in synaptic function, particularly those involved in the regulation of membrane potential, ion balance and receptor signalling. Hippocampal long-term potentiation was impaired before and soon after the onset of cognitive deficits, at 1 and 4 weeks post-inoculation, respectively. L-DOPA recovered hippocampal long-term potentiation more efficiently at 4 weeks post-inoculation than pramipexole, which partially rescued it at both time points. Overall, we found impaired synaptic plasticity and proteome dysregulation at hippocampal terminals to be the first events that contribute to the development of cognitive deficits in experimental parkinsonism. Our results not only point to dopaminergic but also to glutamatergic and GABAergic dysfunction, highlighting the relevance of the three neurotransmitter systems in the ventral tegmental area-hippocampus interaction from the earliest stages of parkinsonism. The proteins identified in the current work may constitute potential biomarkers of early synaptic damage in the hippocampus and hence, therapies targeting these could potentially restore early synaptic malfunction and consequently, cognitive deficits in Parkinson's disease.

Adeno-associated virus (AAV) 9-mediated gene delivery of Nurr1 and Foxa2 ameliorates symptoms and pathologies of Alzheimer disease model mice by suppressing neuro-inflammation and glial pathology

There is a compelling need to develop disease-modifying therapies for Alzheimer's disease (AD), the most common neuro-degenerative disorder. Together with recent progress in vector development for efficiently targeting the central nervous system, gene therapy has been suggested as a potential therapeutic modality to overcome the limited delivery of conventional types of drugs to and within the damaged brain. In addition, given increasing evidence of the strong link between glia and AD pathophysiology, therapeutic targets have been moving toward those addressing glial cell pathology. Nurr1 and Foxa2 are transcription/epigenetic regulators that have been reported to cooperatively regulate inflammatory and neurotrophic response in glial cells. In this study, we tested the therapeutic potential of Nurr1 and Foxa2 gene delivery to treat AD symptoms and pathologies. A series of functional, histologic, and transcriptome analyses revealed that the combined expression of Nurr1 and Foxa2 substantially ameliorated AD-associated amyloid β and Tau proteinopathy, cell senescence, synaptic loss, and neuro-inflammation in multiple in vitro and in vivo AD models. Intra-cranial delivery of Nurr1 and Foxa2 genes using adeno-associated virus (AAV) serotype 9 improved the memory and cognitive function of AD model mice. The therapeutic benefits of gene delivery were attained mainly by correcting pathologic glial function. These findings collectively indicate that AAV9-mediated Nurr1 and Foxa2 gene transfer could be an effective disease-modifying therapy for AD.

Ferroelectric artificial synapse for neuromorphic computing and flexible applications

Research of artificial synapses is increasing in popularity with the development of bioelectronics and the appearance of wearable devices. Because the high-temperature treatment process of inorganic materials is not compatible with flexible substrates, organic ferroelectric materials that are easier to process have emerged as alternatives. An organic synaptic device based on P(VDF-TrFE) was prepared in this study. The device showed reliable P/E endurance over 104 cycles and a data storage retention capability at 80 °C over 104 s. Simultaneously, it possessed excellent synaptic functions, including short-term/ long-term synaptic plasticity and spike-timing-dependent plasticity. In addition, the ferroelectric performance of the device remained stable even under bending (7 mm bending radius) or after 500 bending cycles. This work shows that low-temperature processed organic ferroelectric materials can provide new ideas for the future development of wearable electronics and flexible artificial synapses.

Experience-Induced Remodeling of the Hippocampal Post-synaptic Proteome and Phosphoproteome

The postsynaptic density (PSD) of excitatory synapses contains a highly organized protein network with thousands of proteins and is a key node in the regulation of synaptic plasticity. To gain new mechanistic insight into experience-induced changes in the PSD, we examined the global dynamics of the hippocampal PSD proteome and phosphoproteome in mice following four different types of experience. Mice were trained using an inhibitory avoidance (IA) task and hippocampal PSD fractions were isolated from individual mice to investigate molecular mechanisms underlying experience-dependent remodeling of synapses. We developed a new strategy to identify and quantify the relatively low level of site-specific phosphorylation of PSD proteome from the hippocampus, by using a modified iTRAQ-based TiSH protocol. In the PSD, we identified 3938 proteins and 2761 phosphoproteins in the sequential strategy covering a total of 4968 unique protein groups (at least two peptides including a unique peptide). On the phosphoproteins, we identified a total of 6188 unambiguous phosphosites (75% Collapse

Key Words

• inhibitory avoidance
• learning and memory
• phosphoproteomics
• postsynaptic density

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MESH Headings

• Mice
• Animals
• Proteome/metabolism
• Membrane Proteins/metabolism
• Nerve Tissue Proteins/metabolism
• Hippocampus/metabolism
• Synapses/metabolism
• Peptides/metabolism
• Phosphoproteins/metabolism
• Disks Large Homolog 4 Protein/metabolism

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Grants

• R01 MH112152 NIMH NIH HHS
• R01 NS036715 NINDS NIH HHS

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Affiliation(s)

Seok Heo Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, Maryland, USA
Taewook Kang Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
Alexei M Bygrave Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, Maryland, USA
Martin R Larsen Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark.
Richard L Huganir Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, Maryland, USA.

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Collaborators Collapse

All-Optical Data Processing with Photon-Avalanching Nanocrystalline Photonic Synapse

Data processing and storage in electronic devices are typically performed as a sequence of elementary binary operations. Alternative approaches, such as neuromorphic or reservoir computing, are rapidly gaining interest where data processing is relatively slow, but can be performed in a more comprehensive way or massively in parallel, like in neuronal circuits. Here, time-domain all-optical information processing capabilities of photon-avalanching (PA) nanoparticles at room temperature are discovered. Demonstrated functionality resembles properties found in neuronal synapses, such as: paired-pulse facilitation and short-term internal memory, in situ plasticity, multiple inputs processing, and all-or-nothing threshold response. The PA-memory-like behavior shows capability of machine-learning-algorithm-free feature extraction and further recognition of 2D patterns with simple 2 input artificial neural network. Additionally, high nonlinearity of luminescence intensity in response to photoexcitation mimics and enhances spike-timing-dependent plasticity that is coherent in nature with the way a sound source is localized in animal neuronal circuits. Not only are yet unexplored fundamental properties of photon-avalanche luminescence kinetics studied, but this approach, combined with recent achievements in photonics, light confinement and guiding, promises all-optical data processing, control, adaptive responsivity, and storage on photonic chips.

A Hybrid Transistor with Transcriptionally Controlled Computation and Plasticity

Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.

Polysilicon-Channel Synaptic Transistors for Implementation of Short- and Long-Term Memory Characteristics

The rapid progress of artificial neural networks (ANN) is largely attributed to the development of the rectified linear unit (ReLU) activation function. However, the implementation of software-based ANNs, such as convolutional neural networks (CNN), within the von Neumann architecture faces limitations due to its sequential processing mechanism. To overcome this challenge, research on hardware neuromorphic systems based on spiking neural networks (SNN) has gained significant interest. Artificial synapse, a crucial building block in these systems, has predominantly utilized resistive memory-based memristors. However, the two-terminal structure of memristors presents difficulties in processing feedback signals from the post-synaptic neuron, and without an additional rectifying device it is challenging to prevent sneak current paths. In this paper, we propose a four-terminal synaptic transistor with an asymmetric dual-gate structure as a solution to the limitations of two-terminal memristors. Similar to biological synapses, the proposed device multiplies the presynaptic input signal with stored synaptic weight information and transmits the result to the postsynaptic neuron. Weight modulation is explored through both hot carrier injection (HCI) and Fowler-Nordheim (FN) tunneling. Moreover, we investigate the incorporation of short-term memory properties by adopting polysilicon grain boundaries as temporary storage. It is anticipated that the devised synaptic devices, possessing both short-term and long-term memory characteristics, will enable the implementation of various novel ANN algorithms.

mRNA isoform balance in neuronal development and disease

Balanced mRNA isoform diversity and abundance are spatially and temporally regulated throughout cellular differentiation. The proportion of expressed isoforms contributes to cell type specification and determines key properties of the differentiated cells. Neurons are unique cell types with intricate developmental programs, characteristic cellular morphologies, and electrophysiological potential. Neuron-specific gene expression programs establish these distinctive cellular characteristics and drive diversity among neuronal subtypes. Genes with neuron-specific alternative processing are enriched in key neuronal functions, including synaptic proteins, adhesion molecules, and scaffold proteins. Despite the similarity of neuronal gene expression programs, each neuronal subclass can be distinguished by unique alternative mRNA processing events. Alternative processing of developmentally important transcripts alters coding and regulatory information, including interaction domains, transcript stability, subcellular localization, and targeting by RNA binding proteins. Fine-tuning of mRNA processing is essential for neuronal activity and maintenance. Thus, the focus of neuronal RNA biology research is to dissect the transcriptomic mechanisms that underlie neuronal homeostasis, and consequently, predispose neuronal subtypes to disease. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA in Disease and Development > RNA in Development.

Recent progress in neuromorphic and memory devices based on graphdiyne

Graphdiyne (GDY) is an emerging two-dimensional carbon allotrope featuring a direct bandgap and fascinating physical and chemical properties, and it has demonstrated its promising potential in applications of catalysis, energy conversion and storage, electrical/optoelectronic devices, etc. In particular, the recent breakthrough in the synthesis of large-area, high-quality and ultrathin GDY films provides a feasible approach to developing high-performance electrical devices based on GDY. Recently, various GDY-based electrical and optoelectronic devices including multibit optoelectronic memories, ultrafast nonvolatile memories, artificial synapses and memristors have been proposed, in which GDY plays a crucial role. It is essential to summarize the recent breakthrough of GDY in device applications as a guidance, especially considering that the existing GDY-related reviews mainly focus on the applications in catalysis and energy-related fields. Herein, we review GDY-based novel memory and neuromorphic devices and their applications in neuromorphic computing and artificial visual systems. This review will provide an insight into the design and preparation of GDY-based devices and broaden the application fields of GDY.

Denervated mouse CA1 pyramidal neurons express homeostatic synaptic plasticity following entorhinal cortex lesion

Structural, functional, and molecular reorganization of denervated neural networks is often observed in neurological conditions. The loss of input is accompanied by homeostatic synaptic adaptations, which can affect the reorganization process. A major challenge of denervation-induced homeostatic plasticity operating in complex neural networks is the specialization of neuronal inputs. It remains unclear whether neurons respond similarly to the loss of distinct inputs. Here, we used in vitro entorhinal cortex lesion (ECL) and Schaffer collateral lesion (SCL) in mouse organotypic entorhino-hippocampal tissue cultures to study denervation-induced plasticity of CA1 pyramidal neurons. We observed microglia accumulation, presynaptic bouton degeneration, and a reduction in dendritic spine numbers in the denervated layers 3 days after SCL and ECL. Transcriptome analysis of the CA1 region revealed complex changes in differential gene expression following SCL and ECL compared to non-lesioned controls with a specific enrichment of differentially expressed synapse-related genes observed after ECL. Consistent with this finding, denervation-induced homeostatic plasticity of excitatory synapses was observed 3 days after ECL but not after SCL. Chemogenetic silencing of the EC but not CA3 confirmed the pathway-specific induction of homeostatic synaptic plasticity in CA1. Additionally, increased RNA oxidation was observed after SCL and ECL. These results reveal important commonalities and differences between distinct pathway lesions and demonstrate a pathway-specific induction of denervation-induced homeostatic synaptic plasticity.

Environmentally Stable and Reconfigurable Ultralow-Power Two-Dimensional Tellurene Synaptic Transistor for Neuromorphic Edge Computing

While neuromorphic computing can define a new era for next-generation computing architecture, the introduction of an efficient synaptic transistor for neuromorphic edge computing still remains a challenge. Here, we envision an atomically thin 2D Te synaptic device capable of achieving a desirable neuromorphic edge computing design. The hydrothermally grown 2D Te nanosheet synaptic transistor apparently mimicked the biological synaptic nature, exhibiting 100 effective multilevel states, a low power consumption of ∼110 fJ, excellent linearity, and short-/long-term plasticity. Furthermore, the 2D Te synaptic device achieved reconfigurable MNIST recognition accuracy characteristics of 88.2%, even after harmful detergent environment infection. We believe that this work serves as a guide for developing futuristic neuromorphic edge computing.

Inorganic Perovskite Quantum Dot-Mediated Photonic Multimodal Synapse

Artificial synapse is the basic unit of a neuromorphic computing system. However, there is a need to explore suitable synaptic devices for the emulation of synaptic dynamics. This study demonstrates a photonic multimodal synaptic device by implementing a perovskite quantum dot charge-trapping layer in the organic poly(3-hexylthiophene-2,5-diyl) (P3HT) channel transistor. The proposed device presents favorable band alignment that facilitates spatial separation of photogenerated charge carriers. The band alignment serves as the basis of optically induced charge trapping, which enables nonvolatile memory characteristics in the device. Furthermore, high photoresponse and excellent synaptic characteristics, such as short-term plasticity, long-term plasticity, excitatory postsynaptic current, and paired-pulse facilitation, are obtained through gate voltage regulation. Photosynaptic characteristics obtained from the device showed a multiwavelength response and a large dynamic range (∼10³) that is suitable for realizing a highly accurate artificial neural network. Moreover, the device showed nearly linear synaptic weight update characteristics with incremental depression electric gate pulse. The simulation based on the experimental data showed excellent pattern recognition accuracy (∼85%) after 120 epochs. The results of this study demonstrate the feasibility of the device as an optical synapse in the next-generation neuromorphic system.