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Here, we demonstrate double-layer 3D vertical resistive random-access memory with a hole-type structure embedding Pt/HfOx/AlN/TiN memory cells, conduct analog resistive switching, and examine the potential of memristors for use in neuromorphic systems. The electrical characteristics, including resistive switching, retention, and endurance, of each layer are also obtained. Additionally, we investigate various synaptic characteristics, such as spike-timing dependent plasticity, spike-amplitude dependent plasticity, spike-rate dependent plasticity, spike-duration dependent plasticity, and spike-number dependent plasticity. This synapse emulation holds great potential for neuromorphic computing applications. Furthermore, potentiation and depression are manifested through identical pulses based on DC resistive switching. The pattern recognition rates within the neural network are evaluated, and based on the conductance changing linearly with incremental pulses, we achieve a pattern recognition accuracy of over 95%. Finally, the device's stability and synapse characteristics exhibit excellent potential for use in neuromorphic systems. [ABSTRACT FROM AUTHOR]

Information processing and memorizing in the brain take place in a neural network consisting of neurons connected with each other by synapses. Meanwhile, the neural network is immersed in a common electrochemical environment with global parameters regulating the overall functions, which is barely discussed in neuromorphic devices. In this study, organic/inorganic hybrid transistors with sodium alginate as the gate dielectric layer and indium tin oxide as the channel were successfully prepared. We have not only simulated the basic properties of synapses in a single device, but, on top of that, also simulated the global regulation of information processing in the brain due to the incorporation of global grids, achieving excitatory and inhibitory synaptic weight. Moreover, the construction of a 3 × 3 synaptic array enables image learning and memorizing functions. These results demonstrate the significant advantages of electrolyte-gated transistors in enabling complex neural network connectivity and offer a promising opportunity for future artificial synapses. [ABSTRACT FROM AUTHOR]

This study presents a preliminary exploration of thermally oxidized TaOx-based memristors and their potential as artificial synapses. Unlike the 10-min annealed devices, which display instability due to current overshoots, the 5-min annealed device exhibits stable resistive switching, retention, and endurance characteristics. Moreover, our memristor showcases synaptic behaviors encompassing potentiation, depression, spike-timing-dependent plasticity, and excitatory postsynaptic currents. This synaptic emulation holds tremendous promise for applications in neuromorphic computing, offering the opportunity to replicate the adaptive learning principles observed in biological synapses. In addition, we evaluate the device's suitability for pattern recognition within a neural network using the modified National Institute of Standards and Technology dataset. Our assessment reveals that the Pt/TaOx/Ta memristor with an oxidized insulator achieves outstanding potential manifested by an accuracy of 93.25% for the identical pulse scheme and an impressive accuracy of 95.42% for the incremental pulse scheme. [ABSTRACT FROM AUTHOR]

Perovskite‐type rare earth nickelates based memristor have recently attracted extensive attention in the field of novel storage computing due to their special electronic structure and exotic physical properties. However, there is still a shortage of memristors with ultra‐high stability performance, which will provide a solid foundation for future neural network computing with high accuracy recognition rates. Here, a GdNiO3‐based interfacial memristor is presented, which possesses ultra‐high stable performance, such as electroforming‐free, low device‐to‐device variation, reliable cyclic switching, high on/off ratio (≈104) and stable pulse modulation of conduction. Combined with the comprehensive microstructure results, this behavior is ascribed to the interface Schottky barrier variation caused by the 1D oxygen vacancy channel conduction according to the transmission electron microscopy results. In particular, based on the device's stable pulse modulation plasticity performance, the study also succeeds in achieving highly accurate neural firing pattern recognition up to ≈99.75% accuracy and monitoring of pattern transitions by implementing a reservoir computing system based on the device. This research advances the progress of nickelates in novel storage computing and paves the way for future efficient memristor‐based reservoir computing systems to handle more complex temporal tasks. [ABSTRACT FROM AUTHOR]

Bionic visual systems require multimodal integration of eye‐like photodetectors and brain‐like image memory. However, the integration of photodetectors (PDs) and artificial optoelectronic synapses devices (OESDs) by one device remains a giant challenge due to their photoresponse discrepancy. Herein, a dual‐functional integration of PDs and OESDs based on VO2/WO3 heterojunctions is presented. The device can be able to realize a dual‐mode conversion between PDs and OESDs through tuning the bias voltage. Under zero bias voltage, the device exhibiting excellent photodetecting behaviors based on the photovoltaic effect, showing a high self‐powered photoresponsivity of 18.5 mA W−1 and high detectivity of 7.5 × 1010 Jones with fast photoresponse. When the external bias voltages are applied, it can be acted as an OESD and exhibit versatile electrical and photonic synaptic characteristics based on the trapping and detrapping effects, including synaptic plasticity and learning‐experience behaviors. More importantly, benefiting from the excellent photosensing ability and transporting properties, the device shows ultralow‐power consumption of 39.0 pJ and a 4 × 4 OESDs array is developed to realize the visual perception and memory. This work not only supplies a novel route to realize complex functional integration just in one device, but also offers effective strategies for developing neuromorphic visual system. [ABSTRACT FROM AUTHOR]

2D van der Waals heterojunctions (vdWH) have emerged as an attractive platform for the realization of optoelectronic synaptic devices, which are critical for energy‐efficient computing systems. Photogating induced by charge traps at the interfaces indeed results in ultrahigh responsivity and tunable photoconductance. Yet, optical potentiation and depression remain mostly modulated by gate bias, requiring relatively high energy inputs. Thus, advanced all‐optical synapse switching strategies are still needed. In this work, a reversible switching between positive photoconductivity (PPC) and negative photoconductivity (NPC) is achieved in graphene/WSe2 vdWH solely through light‐intensity modulation. Consequently, the graphene/WSe2 synaptic device shows tunable optical potentiation and depression behavior with an ultralow power consumption of 127 aJ. The study further unravels the complex interplay of gate bias and incident light power in determining the sign and magnitude of the photocurrent, showing the critical role of charge trapping and photogating at interfaces. Interestingly, it is found that switching between PPC to NPC can be also obtained at 0 mV drain‐source voltage. Overall, the reversible potentiation/depression effect based on light intensity modulation and its combination with additional gate bias tunability is very appealing for the development of energy‐efficient optical communications and neuromorphic computing. [ABSTRACT FROM AUTHOR]

Neuromorphic visual systems based on optogenetic techniques have colossal potential for in‐memory computing with prospects of developing artificial intelligence vision systems. However, conventional transistor architectures face formidable challenges in efficient signal processing owing to limitations in the intrinsic properties of active channel materials. In this work, a novel transition metal telluride‐sulfide hybrid heterojunction‐based optoelectronic synaptic phototransistor is proposed, in which UV–vis responsive zinc oxide encapsulated few‐layer tungsten disulfide channel is decorated with near‐infrared sensitive 0D cobalt ditelluride (CoTe2) nanocrystals (NCs), eliciting the ability to sense, store, and process optical signals across a broad range of the electromagnetic spectrum. This meticulously designed three‐layered heterostructure, based on their interfacial band alignments, enables high photoresponsivity up to ≈2.6 × 103 A W−1 at a back‐gate bias of 20 V, leading to the brain‐inspired synaptic applications with an average power consumption as low as 75 pJ for each training process. The device exhibits excitatory postsynaptic current, paired‐pulse facilitation with an index above 150%, as well as light‐modulated synaptic plasticity by mimicking biological synapses, which mainly originate from trapped holes in Co‐vacancy mediated surface defect states of CoTe2 NCs. Hence, this 2D material‐based hybrid phototransistor appears to be a promising candidate for energy‐efficient next‐generation brain‐inspired neuromorphic vision systems. [ABSTRACT FROM AUTHOR]

High‐entropy oxides are garnering increased attention due to their unique properties engineered by chemical disorders. Chemical disorders can cause random potential spatial fluctuations and the resulting anomalous photoconductivity, while they have not yet been exploited in high‐entropy oxides for usefulness. In this work, persistent photoconductivity (PPC) behavior in high‐entropy perovskite Ba(Zr0.2Sn0.2Ti0.2Hf0.2Nb0.2)O3 thin films is reported and used it as a basis to develop artificial photonic synapses. This photonic synapse can emulate distinctive biological synaptic features such as excitatory postsynaptic current, paired‐pulse facilitation, transition from short‐term memory to long‐term memory, and learning‐forgetting processes. This photonic synapse can also implement the basic "AND" and "OR" logic operations. In addition, a 5×5 photonic synaptic array is built to mimic the spatiotemporal sensing and memory functions of a visual sensing‐memory system, exhibiting the improved accuracy of dynamic machine vision classification tasks as compared to traditional visual sensing systems. This study highlights the potential of high‐entropy oxides for application in neuromorphic computing and dynamic machine vision systems. [ABSTRACT FROM AUTHOR]

Ripples are a typical form of neural activity in hippocampal neural networks associated with the replay of episodic memories during sleep as well as sleep-related plasticity and memory consolidation. The emergence of ripples has been observed both dependent as well as independent of input from other brain areas and often coincides with dendritic spikes. Yet, it is unclear how input-evoked and spontaneous ripples as well as dendritic excitability affect plasticity and consolidation. Here, we use mathematical modeling to compare these cases. We find that consolidation as well as the emergence of spontaneous ripples depends on a reliable propagation of activity in feed-forward structures which constitute memory representations. This propagation is facilitated by excitable dendrites, which entail that a few strong synapses are sufficient to trigger neuronal firing. In this situation, stimulation-evoked ripples lead to the potentiation of weak synapses within the feed-forward structure and, thus, to a consolidation of a more general sequence memory. However, spontaneous ripples that occur without stimulation, only consolidate a sparse backbone of the existing strong feed-forward structure. Based on this, we test a recently hypothesized scenario in which the excitability of dendrites is transiently enhanced after learning, and show that such a transient increase can strengthen, restructure and consolidate even weak hippocampal memories, which would be forgotten otherwise. Hence, a transient increase in dendritic excitability would indeed provide a mechanism for stabilizing memories. Author summary: Sequential activity of place cells in hippocampus has been found to be replayed during sleep. Such replay is believed to aid the consolidation of episodic memories. We here investigate replay of memories encoded by a feed-forward structure within a recurrent network as well as associated synaptic plasticity. Hereby, we vary both the external stimulation that triggers replay-sequences, as well as the excitability of active dendrites. With stimulation and highly excitable dendrites, we observe reliable replays and potentiation of both weak and strong synapses along the feed-forward structure. Without stimulation but with highly excitable dendrites, spontaneous replays emerge and only sufficiently strong synapses are further potentiated. Without highly excitable dendrites, the memory decays. However, with an intermediate period of increased dendritic excitability, the memory can be sufficiently restructured and strengthened to exhibit spontaneous replays and consolidation, even at lower dendritic excitability levels. These results indicate that a transient increase in dendritic excitability provides a mechanism both for restructuring and stabilizing hippocampal memories. [ABSTRACT FROM AUTHOR]

A novel polarization‐sensitive artificial optoelectronic synapse based on β‐Ga2O3 single‐crystal is proposed in this work, featuring reconfigurable anisotropic vision. A series of polarization‐sensitive synaptic activities and polarization‐sensitive image recognition functions are successfully simulated using this device. The intriguing performance of this device, stems from the crystal anisotropy in β‐Ga2O3, which is confirmed through polarization Raman measurements and first‐principles theoretical calculations. Furthermore, a comprehensive analysis of the persistent photoelectric properties of the device unveils that the adaptability of the optoelectronic synapse device stems from the ionization and dissociation of oxygen vacancies. Ultimately, the device is utilized in the fields of image recognition and information encryption. A four‐layer artificial neural network with two hidden layers is constructed for recognizing handwritten digits. After training, the recognition accuracy reaches over 91.5% for both unpolarized and polarized light. Information encryption is achieved by controlling polarization states. The device enables data generation and encryption to be conducted on the same platform, mitigating exposure risks during transmission and significantly enhancing data security and confidentiality. This work presents new opportunities for future applications of polarization‐based perception systems. [ABSTRACT FROM AUTHOR]

The human visual system to process real‐time light signals and safeguard against excessive light exposure serves as a model for artificial vision systems. Despite the wide use of optoelectronic synaptic devices in such simulations, their complex circuitry and high energy consumption have impeded further development. Herein, a biomimetic ocular system utilizing organic all‐photonic synapses is constructed to realize sensation, memory, processing, and protective light reflex abilities. The synaptic performances are facially regulated by simple molecular engineering to afford a record PPF value of 430%. Impressively, a large area (400 cm2) synaptic device with a high uniformity (96%) exhibited four fundamental functions of an ocular system for the first time, which significantly simplified the circuit system and reduced energy consumption. This work provides a facial strategy to construct all‐photonic synapses, holding great potential in prosthetics and neurorobotics. [ABSTRACT FROM AUTHOR]

The rapid development of artificial intelligence requires synaptic devices with controllable conductance updates and low power consumption. Currently, conductance updates based on identical voltage pulse scheme (IVPS) and nonidentical voltage pulse scheme (NIVPS) face drawbacks in terms of recognition accuracy and energy efficiency, respectively. In this study, a mixed voltage pulse scheme (MVPS) for tuning conductance is proposed to simultaneously achieve high recognition accuracy and high energy efficiency, and its superiority is experimentally verified based on high‐performance Au (or Ag)/PbZr0.52Ti0.48O3/Nb:SrTiO3 ferroelectric tunnel junction (FTJ) synaptic devices. The MVPS‐based neural network simulation achieves a high recognition accuracy of ≈92% on the CIFAR10 dataset with better energy efficiency and throughput than those of NIVPS. In addition, high‐precision experimental vector‐matrix multiplication (with a relative error of ≈1.5%) is obtained, and the simulated FTJ synaptic arrays achieved a high inference energy efficiency of ≈85 TOPS W−1 and a throughput of ≈200 TOPS, which meets the requirements of artificial intelligence in low‐power scenarios. This study provides a possible solution for practical applications of FTJ in neural network computing. [ABSTRACT FROM AUTHOR]

Vertebrate vision begins with light absorption by rod and cone photoreceptors, which transmit signals from their synaptic terminals to second-order neurons: bipolar and horizontal cells. In mouse rods, there is a single presynaptic ribbon-type active zone at which the release of glutamate occurs tonically in the dark. This tonic glutamatergic signaling requires continuous exo- and endocytosis of synaptic vesicles. At conventional synapses, endocytosis commonly requires dynamins: GTPases encoded by three genes (Dnm1-3), which perform membrane scission. Disrupting endocytosis by dynamin deletions impairs transmission at conventional synapses, but the impact of disrupting endocytosis and the role(s) of specific dynamin isoforms at rod ribbon synapses are understood incompletely. Here, we used cell-specific knock-outs (KOs) of the neuron-specific Dnm1 and Dnm3 to investigate the functional roles of dynamin isoforms in rod photoreceptors in mice of either sex. Analysis of synaptic protein expression, synapse ultrastructure, and retinal function via electroretinograms (ERGs) showed that dynamins 1 and 3 act redundantly and are essential for supporting the structural and functional integrity of rod ribbon synapses. Single Dnm3 KO showed no phenotype, and single Dnm1 KO only modestly reduced synaptic vesicle density without affecting vesicle size and overall synapse integrity, whereas double Dnm1/Dnm3 KO impaired vesicle endocytosis profoundly, causing enlarged vesicles, reduced vesicle density, reduced ERG responses, synaptic terminal degeneration, and disassembly and degeneration of postsynaptic processes. Concurrently, cone function remained intact. These results show the fundamental redundancy of dynamins 1 and 3 in regulating the structure and function of rod ribbon synapses. [ABSTRACT FROM AUTHOR]

Metal halide perovskites (MHPs) are considered as promising candidates in the application of nonvolatile high‐density, low‐cost resistive switching (RS) memories and artificial synapses, resulting from their excellent electronic and optoelectronic properties including large light absorption coefficient, fast ion migration, long carrier diffusion length, low trap density, high defect tolerance. Among MHPs, 2D halide perovskites have exotic layered structure and great environment stability as compared with 3D counterparts. Herein, recent advances of 2D MHPs for the RS memories and artificial synapses realms are comprehensively summarized and discussed, as well as the layered structure properties and the related physical mechanisms are presented. Furthermore, the current issues and developing roadmap for the next‐generation 2D MHPs RS memories and artificial synapse are elucidated. [ABSTRACT FROM AUTHOR]

Two‐dimensional (2D) ferroionics is appealing in performing complex artificial intelligence tasks due to the interesting property of coexistence of ferroelectricity and ionic activities. CuInP2S6 (CIPS), as a typical 2D ferroionic material, is highly conducive to rich functions of information devices due to the displacement of Cu+ inducing both ferroelectricity and ionic conductivity. However, the coupling and modulation of polarization and ion migration in CIPS for multifunctional information devices has not been fully explored. Here, this study demonstrates that digital memory and synaptic simulations are realized in Au/CIPS/Au device via engineering ferroelectric polarization reversal and the long‐distance migration of the Cu+ to change conductive modes. Steep resistive switching behavior based on ion‐migration is observed with a high on/off ratio of over 108, long retention time (>2 × 104 s), and current compliance engineered multilevel resistance states, demonstrating reliable nonvolatile high‐density memory characteristics. Based on the continuous modulation of polar order, the key synaptic behaviors are successfully simulated. Moreover, by the co‐modulation of polarization state and ions migration, the paired‐pulse facilitation, paired‐pulse depression, and potentiation following depression are achieved. These results suggest that CIPS is a promising candidate for constructing high‐performance, function‐enriched devices for data storage, information processing, and neuromorphic computing. [ABSTRACT FROM AUTHOR]

Neurons in the central nervous system communicate with each other by activating billions of tiny synaptic boutons distributed along their fine axons. These presynaptic varicosities are very crowded environments, comprising hundreds of synaptic vesicles. Only a fraction of these vesicles can be recruited in a single release episode, either spontaneous or evoked by action potentials. Since the seminal work by Fatt and Katz, spontaneous release has been modelled as a memoryless process. Nevertheless, at central synapses, experimental evidence indicates more complex features, including non‐exponential distributions of release intervals and power‐law behaviour in their rate. To describe these features, we developed a probabilistic model of spontaneous release based on Brownian motion of synaptic vesicles in the presynaptic environment. To account for different diffusion regimes, we based our simulations on fractional Brownian motion. We show that this model can predict both deviation from the Poisson hypothesis and power‐law features in experimental quantal release series, thus suggesting that the vesicular motion by diffusion could per se explain the emergence of these properties. We demonstrate the efficacy of our modelling approach using electrophysiological recordings at single synaptic boutons and ultrastructural data. When this approach was used to simulate evoked responses, we found that the replenishment of the readily releasable pool driven by Brownian motion of vesicles can reproduce the characteristic binomial release distributions seen experimentally. We believe that our modelling approach supports the idea that vesicle diffusion and readily releasable pool dynamics are crucial factors for the physiological functioning of neuronal communication. Key points: We developed a new probabilistic model of spontaneous and evoked vesicle fusion based on simple biophysical assumptions, including the motion of vesicles before they dock to the release site.We provide closed‐form equations for the interval distribution of spontaneous releases in the special case of Brownian diffusion of vesicles, showing that a power‐law heavy tail is generated.Fractional Brownian motion (fBm) was exploited to simulate anomalous vesicle diffusion, including directed and non‐directed motion, by varying the Hurst exponent.We show that our model predicts non‐linear features observed in experimental spontaneous quantal release series as well as ultrastructural data of synaptic vesicles spatial distribution.Evoked exocytosis based on a diffusion‐replenished readily releasable pool might explain the emergence of power‐law behaviour in neuronal activity. [ABSTRACT FROM AUTHOR]

Controlled cross‐linking of polymer dielectric poly (4‐vinylphenol) (PVP) is demonstrated as an effective tool in enhancing the performance of flexible organic synaptic transistors (OSTs). Investigation of variation of concentration of the cross‐linking agent methylated poly (melamine‐co‐formaldehyde) (PMCF) in PVP in bilayer combination with high‐k hafnium oxide (HfO2) as gate dielectric in devices shows that the lower concentration of cross‐linking agent results in better memory performance. OSTs with 26% PMCF concentration in PVP (by mass) exhibit excellent memory performance with memory window > 4 V for VGS sweep of ±5 V, static retention of ≈104 s, dynamic retention for 500 cycles, and ≈125 continuous program/erase cycles. Pulse paired facilitation with relaxation time constants of 370 and 4670 ms respectively for slow and rapid phases with regulating modulation amplitude of ≈1 resemble a biological synapse. Through excitatory post synaptic current characteristics, spike timing dependant plasticity and spike voltage dependant plasticity are clearly observed, with low energy consumption per spike on the order of 10 pJ. Further, by leveraging the intricate interconnected data transfer and computation phenomenon, “AND” logic is effectively implemented using these OSTs. These exciting results may open up new directions toward the development of hardware for neuromorphic computing. [ABSTRACT FROM AUTHOR]

Background: Mutations in the X-linked gene cyclin-dependent kinase-like 5 (CDKL5) cause a severe neurological disorder characterised by early-onset epileptic seizures, autism and intellectual disability (ID). Impaired hippocampal function has been implicated in other models of monogenic forms of autism spectrum disorders and ID and is often linked to epilepsy and behavioural abnormalities. Many individuals with CDKL5 deficiency disorder (CDD) have null mutations and complete loss of CDKL5 protein, therefore in the current study we used a Cdkl5−/y rat model to elucidate the impact of CDKL5 loss on cellular excitability and synaptic function of CA1 pyramidal cells (PCs). We hypothesised abnormal pre and/or post synaptic function and plasticity would be observed in the hippocampus of Cdkl5−/y rats. Methods: To allow cross-species comparisons of phenotypes associated with the loss of CDKL5, we generated a loss of function mutation in exon 8 of the rat Cdkl5 gene and assessed the impact of the loss of CDLK5 using a combination of extracellular and whole-cell electrophysiological recordings, biochemistry, and histology. Results: Our results indicate that CA1 hippocampal long-term potentiation (LTP) is enhanced in slices prepared from juvenile, but not adult, Cdkl5−/y rats. Enhanced LTP does not result from changes in NMDA receptor function or subunit expression as these remain unaltered throughout development. Furthermore, Ca2+ permeable AMPA receptor mediated currents are unchanged in Cdkl5−/y rats. We observe reduced mEPSC frequency accompanied by increased spine density in basal dendrites of CA1 PCs, however we find no evidence supporting an increase in silent synapses when assessed using a minimal stimulation protocol in slices. Additionally, we found no change in paired-pulse ratio, consistent with normal release probability at Schaffer collateral to CA1 PC synapses. Conclusions: Our data indicate a role for CDKL5 in hippocampal synaptic function and raise the possibility that altered intracellular signalling rather than synaptic deficits contribute to the altered plasticity. Limitations: This study has focussed on the electrophysiological and anatomical properties of hippocampal CA1 PCs across early postnatal development. Studies involving other brain regions, older animals and behavioural phenotypes associated with the loss of CDKL5 are needed to understand the pathophysiology of CDD. [ABSTRACT FROM AUTHOR]

Synapse maintenance is essential for generating functional circuitry, and decrement in this process is a hallmark of neurodegenerative disease. Yet, little is known about synapse maintenance in vivo. Cysteine string protein a (CSPa), encoded by the Dnajc5 gene, is a synaptic vesicle chaperone that is necessary for synapse maintenance and linked to neurodegeneration. To investigate the transcriptional changes associated with synapse maintenance, we performed single-nucleus transcriptomics on the cortex of young CSPa knockout (KO) mice and littermate controls. Through differential expression and gene ontology analysis, we observed that both neurons and glial cells exhibit unique signatures in the CSPa KO brain. Significantly, all neuronal classes in CSPa KO brains show strong signatures of repression in synaptic pathways, while up-regulating autophagy-related genes. Through visualization of synapses and autophagosomes by electron microscopy, we confirmed these alterations especially in inhibitory synapses. Glial responses varied by cell type, with microglia exhibiting activation. By imputing cell-cell interactions, we found that neuron-glia interactions were specifically increased in CSPa KO mice. This was mediated by synaptogenic adhesion molecules, with the classical Neurexin1-Neuroligin 1 pair being the most prominent, suggesting that communication of glial cells with neurons is strengthened in CSPa KO mice to preserve synapse maintenance. Together, this study provides a rich dataset of transcriptional changes in the CSPa KO cortex and reveals insights into synapse maintenance and neurodegeneration. [ABSTRACT FROM AUTHOR]

General anesthetics disrupt brain network dynamics through multiple pathways, in part through postsynaptic potentiation of inhibitory ion channels as well as presynaptic inhibition of neuroexocytosis. Common clinical general anesthetic drugs, such as propofol and isoflurane, have been shown to interact and interfere with core components of the exocytic release machinery to cause impaired neurotransmitter release. Recent studies however suggest that these drugs do not affect all synapse subtypes equally. We investigated the role of the presynaptic release machinery in multiple neurotransmitter systems under isoflurane general anesthesia in the adult female Drosophila brain using live-cell super–resolution microscopy and optogenetic readouts of exocytosis and neural excitability. We activated neurotransmitter-specific mushroom body output neurons and imaged presynaptic function under isoflurane anesthesia. We found that isoflurane impaired synaptic release and presynaptic protein dynamics in excitatory cholinergic synapses. In contrast, isoflurane had little to no effect on inhibitory GABAergic or glutamatergic synapses. These results present a distinct inhibitory mechanism for general anesthesia, whereby neuroexocytosis is selectively impaired at excitatory synapses, while inhibitory synapses remain functional. This suggests a presynaptic inhibitory mechanism that complements the other inhibitory effects of these drugs. [ABSTRACT FROM AUTHOR]

Understanding the function of the human brain requires determining basic properties of synaptic transmission in human neurons. One of the most fundamental parameters controlling neurotransmitter release is the presynaptic action potential, but its amplitude and duration remain controversial. Presynaptic action potentials have so far been measured with high temporal resolution only in a limited number of vertebrate but not in human neurons. To uncover properties of human presynaptic action potentials, we exploited recently developed tools to generate human glutamatergic neurons by transient expression of Neurogenin 2 (Ngn2) in pluripotent stem cells. During maturation for 3 to 9 weeks of culturing in different established media, the proportion of cells with multiple axon initial segments decreased, while the amount of axonal tau protein and neuronal excitability increased. Super-resolution microscopy revealed the alignment of the pre- and postsynaptic proteins, Bassoon and Homer. Synaptic transmission was surprisingly reliable at frequencies of 20, 50, and 100 Hz. The synchronicity of synaptic transmission during high-frequency transmission increased during 9 weeks of neuronal maturation. To analyze the mechanisms of synchronous high-frequency glutamate release, we developed direct presynaptic patch-clamp recordings from human neurons. The presynaptic action potentials had large overshoots to ∼25 mV and short durations of ∼0.5 ms. Our findings show that Ngn2-induced neurons represent an elegant model system allowing for functional, structural, and molecular analyses of glutamatergic synaptic transmission with high spatiotemporal resolution in human neurons. Furthermore, our data predict that glutamatergic transmission is mediated by large and rapid presynaptic action potentials in the human brain. [ABSTRACT FROM AUTHOR]

A fundamental function of cortical circuits is the integration of information from different sources to form a reliable basis for behavior. While animals behave as if they optimally integrate information according to Bayesian probability theory, the implementation of the required computations in the biological substrate remains unclear. We propose a novel, Bayesian view on the dynamics of conductance-based neurons and synapses which suggests that they are naturally equipped to optimally perform information integration. In our approach apical dendrites represent prior expectations over somatic potentials, while basal dendrites represent likelihoods of somatic potentials. These are parametrized by local quantities, the effective reversal potentials and membrane conductances. We formally demonstrate that under these assumptions the somatic compartment naturally computes the corresponding posterior. We derive a gradient-based plasticity rule, allowing neurons to learn desired target distributions and weight synaptic inputs by their relative reliabilities. Our theory explains various experimental findings on the system and single-cell level related to multi-sensory integration, which we illustrate with simulations. Furthermore, we make experimentally testable predictions on Bayesian dendritic integration and synaptic plasticity. Author summary: The only certainty is uncertainty. Whether it is the reconstruction of a three-dimension scene from the two-dimensional images on our retina or locating your lock in twilight, we have to make decisions and perform actions without knowing the exact state of our environment. In the presence of uncertainty, Bayesian probability theory provides formal recipes of how different pieces of information should be combined to gain maximal information. Indeed, behavioral experiments show that humans and other animals behave as if they operate according to these principles. However, so far it is unclear how the necessary computations are implemented by our biological substrate. By suggesting a new view on the dynamics of a broad class of neuron models, we show how these computations may be implemented by individual cortical neurons. Furthermore, we derive a novel model of synaptic plasticity from first principles and illustrate how a neuron equipped with these synapse dynamics learns to approximate Bayes-optimal decision makers. Finally, we interpret various experimental results in light of our proposed theory and make experimentally testable predictions. [ABSTRACT FROM AUTHOR]

A decade ago, in 2013, and over the course of 4 summer months, three separate observations were reported that each shed light independently on a new molecular organization that fundamentally reshaped our perception of excitatory synaptic transmission (Fukata et al., 2013; MacGillavry et al., 2013; Nair et al., 2013). This discovery unveiled an intricate arrangement of AMPA-type glutamate receptors and their principal scaffolding protein PSD-95, at synapses. This breakthrough was made possible, thanks to advanced super-resolution imaging techniques. It fundamentally changed our understanding of excitatory synaptic architecture and paved the way for a brand-new area of research. In this Progressions article, the primary investigators of the nanoscale organization of synapses have come together to chronicle the tale of their discovery. We recount the initial inquiry that prompted our research, the preceding studies that inspired our work, the technical obstacles that were encountered, and the breakthroughs that were made in the subsequent decade in the realm of nanoscale synaptic transmission. We review the new discoveries made possible by the democratization of super-resolution imaging techniques in the field of excitatory synaptic physiology and architecture, first by the extension to other glutamate receptors and to presynaptic proteins and then by the notion of trans-synaptic organization. After describing the organizational modifications occurring in various pathologies, we discuss briefly the latest technical developments made possible by super-resolution imaging and emerging concepts in synaptic physiology. [ABSTRACT FROM AUTHOR]

Latrophilin-1 (Lphn1, aka CIRL1 and CL1; gene symbol Adgrl1) is an adhesion GPCR that has been implicated in excitatory synaptic transmission as a candidate receptor for α-latrotoxin. Here we analyzed conditional knock-in/knock-out mice for Lphn1 that contain an extracellular myc epitope tag. Mice of both sexes were used in all experiments. Surprisingly, we found that Lphn1 is localized in cultured neurons to synaptic nanoclusters that are present in both excitatory and inhibitory synapses. Conditional deletion of Lphn1 in cultured neurons failed to elicit a detectable impairment in excitatory synapses but produced a decrease in inhibitory synapse numbers and synaptic transmission that was most pronounced for synapses close to the neuronal soma. No changes in axonal or dendritic outgrowth or branching were observed. Our data indicate that Lphn1 is among the few postsynaptic adhesion molecules that are present in both excitatory and inhibitory synapses and that Lphn1 by itself is not essential for excitatory synaptic transmission but is required for some inhibitory synaptic connections. [ABSTRACT FROM AUTHOR]

Photonic synapses integrate sensing and processing within a single device, making them promising candidates for emulating the visual perception of a biological retina. Most retinomorphic photonic synapses achieve image pre‐processing by differentially responding to red, green, and blue (RGB). However, the impact of invisible ultraviolet (UV) light on the retina remains insufficiently explored, hindering the perception of the full spectrum by retinomorphic devices. Herein, a photonic synapse is constructed with strong UV absorption of Poly[(9,9‐dioctylfluorenyl‐2,7‐diyl)‐co‐(4,4′‐(N‐(4‐sec‐butylphenyl) diphenylamine)] (TFB) as photosensitive layer. The device exhibits synaptic properties of long‐term memory (LTM) and short‐term memory (STM) at different UV radiation stages. Extracting the electrical conductance of the device under UV radiation generates patterns of different resolutions, facilitating the simulation of retinal damage's impact on object perception. Moreover, an electronic eye system, incorporating the photonic synapse under UV radiation, successfully replicates pupil constriction and eye‐closing behaviors akin to those observed in a real biological eye. This result underscores the authenticity of retinomorphic devices, expending a novel concept for the future development of visuomorphic computing. [ABSTRACT FROM AUTHOR]

Entering the era of AI 2.0, bio‐inspired target recognition facilitates life. However, target recognition may suffer from some risks when the target is hijacked. Therefore, it is significantly important to provide an encryption process prior to neuromorphic computing. In this work, enlightened from time‐varied synaptic rule, an in‐memory asymmetric encryption as pre‐authentication is utilized with subsequent convolutional neural network (ConvNet) for target recognition, achieving in‐memory two‐factor authentication (IM‐2FA). The unipolar self‐oscillated synaptic behavior is adopted to function as in‐memory asymmetric encryption, which can greatly decrease the complexity of the peripheral circuit compared to bipolar stimulation. Results show that without passing the encryption process with suitable weights at the correct time, the ConvNet for target recognition will not work properly with an extremely low accuracy lower than 0.86%, thus effectively blocking out the potential risks of involuntary access. When a set of correct weights is evolved at a suitable time, a recognition rate as high as 99.82% can be implemented for target recognition, which verifies the effectiveness of the IM‐2FA strategy. [ABSTRACT FROM AUTHOR]

After exocytosis, release sites are cleared of vesicular residues to replenish with transmitter-filled vesicles. Endocytic and scaffold proteins are thought to underlie this site-clearance mechanism. However, the physiological significance of this mechanism at diverse mammalian central synapses remains unknown. Here, we tested this in a physiologically optimized condition using action potential evoked EPSCs at fast calyx synapse and relatively slow hippocampal CA1 synapse, in post-hearing mice brain slices at 37°C and in 1.3 mM [Ca2+]. Pharmacological block of endocytosis enhanced synaptic depression at the calyx synapse, whereas it attenuated synaptic facilitation at the hippocampal synapse. Block of scaffold protein activity likewise enhanced synaptic depression at the calyx but had no effect at the hippocampal synapse. At the fast calyx synapse, block of endocytosis or scaffold protein activity significantly enhanced synaptic depression as early as 10 ms after the stimulation onset. Unlike previous reports, neither endocytic blockers nor scaffold protein inhibitors prolonged the recovery from short-term depression. We conclude that the release-site clearance by endocytosis can be a universal phenomenon supporting vesicle replenishment at both fast and slow synapses, whereas the presynaptic scaffold mechanism likely plays a specialized role in vesicle replenishment predominantly at fast synapses. [ABSTRACT FROM AUTHOR]

Disruption of synapse assembly and maturation leads to a broad spectrum of neurodevelopmental disorders. Presynaptic proteins are largely synthesized in the soma, where they are packaged into precursor vesicles and transported into distal axons to ensure precise assembly and maintenance of presynapses. Due to their morphological features, neurons face challenges in the delivery of presynaptic cargos to nascent boutons. Thus, targeted axonal transport is vital to build functional synapses. A growing number of mutations in genes encoding the transport machinery have been linked to neurodevelopmental disorders. Emerging lines of evidence have started to uncover presynaptic mechanisms underlying axonal transport defects, thus broadening the view of neurodevelopmental disorders beyond postsynaptic mechanisms. In this review, we discuss presynaptic perspectives of neurodevelopmental disorders by focusing on impaired axonal transport and disturbed assembly and maintenance of presynapses. We also discuss potential strategies for restoring axonal transport as an early therapeutic intervention. [ABSTRACT FROM AUTHOR]

Memristive devices such as resistive switching memories and artificial synapses have emerged as promising technologies to overcome the technological challenges associated with the von Neumann bottleneck. Recently, lead halide perovskites have drawn substantial research attention as the candidate material for memristors and artificial synapses due to their unique optoelectronic properties, solution processability, and mechanical flexibility. However, the toxicity of lead‐containing species has raised major concerns for health and the environment, which makes it crucial to transition from lead‐based to lead‐free materials for practical applications. Herein, the recent progress of lead‐free metal halides including perovskites and perovskite analogs for resistive memory and artificial synapse is reviewed. Initially, the fundamentals of lead‐free materials and switching mechanisms are introduced. Next, the material design, fabrication technique, and device performance are summarized and critically evaluated for each metal halide species. Finally, the challenges of the lead‐free metal halides toward memristors and artificial synapses are outlined and discussed, and some potential research directions for future study are proposed. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a common neurodegenerative disease, which is mainly caused by brain lesions. The activation of complement system plays an important role in the process of AD lesions. The activated complement can bind to cell membrane receptors and regulate downstream signals. Therefore, inhibiting complement activation provides a new idea for AD treatment. This article reviews the progress in the mechanism and drug development of complement activation in AD, which may provide a new perspective for the diagnosis, treatment and drug development of AD. [ABSTRACT FROM AUTHOR]

The amygdala (AMY) is widely implicated in fear learning and fear behaviour, but it remains unclear how the many biological components present within AMY interact to achieve these abilities. Building on previous work, we hypothesize that individual AMY nuclei represent different quantities and that fear conditioning arises from error‐driven learning on the synapses between AMY nuclei. We present a computational model of AMY that (a) recreates the divisions and connections between AMY nuclei and their constituent pyramidal and inhibitory neurons; (b) accommodates scalable high‐dimensional representations of external stimuli; (c) learns to associate complex stimuli with the presence (or absence) of an aversive stimulus; (d) preserves feature information when mapping inputs to salience estimates, such that these estimates generalize to similar stimuli; and (e) induces a diverse profile of neural responses within each nucleus. Our model predicts (1) defensive responses and neural activities in several experimental conditions, (2) the consequence of artificially ablating particular nuclei and (3) the tendency to generalize defensive responses to novel stimuli. We test these predictions by comparing model outputs to neural and behavioural data from animals and humans. Despite the relative simplicity of our model, we find significant overlap between simulated and empirical data, which supports our claim that the model captures many of the neural mechanisms that support fear conditioning. We conclude by comparing our model to other computational models and by characterizing the theoretical relationship between pattern separation and fear generalization in healthy versus anxious individuals. [ABSTRACT FROM AUTHOR]

The postsynaptic density (PSD) is a collection of specialized proteins assembled beneath the postsynaptic membrane of dendritic spines. The PSD proteome comprises ~1000 proteins, including neurotransmitter receptors, scaffolding proteins and signalling enzymes. Many of these proteins have essential roles in synaptic function and plasticity. During brain development, changes are observed in synapse density and in the stability and shape of spines, reflecting the underlying molecular maturation of synapses. Synaptic protein composition changes in terms of protein abundance and the assembly of protein complexes, supercomplexes and the physical organization of the PSD. Here, we summarize the developmental alterations of postsynaptic protein composition during synapse maturation. We describe major PSD proteins involved in postsynaptic signalling that regulates synaptic plasticity and discuss the effect of altered expression of these proteins during development. We consider the abnormality of synaptic profiles and synaptic protein composition in the brain in neurodevelopmental disorders such as autism spectrum disorders. We also explain differences in synapse development between rodents and primates in terms of synaptic profiles and protein composition. Finally, we introduce recent findings related to synaptic diversity and nanoarchitecture and discuss their impact on future research. Synaptic protein composition can be considered a major determinant and marker of synapse maturation in normality and disease. [ABSTRACT FROM AUTHOR]

Neural oscillations as synchronized activity of large numbers of neurons in the neural ensembles have always been a hot topic of research for experimental and theoretical studies because of their high importance in human behaviors and functions. Since there is not yet a comprehensive mathematical model for simulating brainwaves, we proposed a cellular automaton (CA) model of a neuronal population by considering the different states of an action potential at the cellular level and simple connectivity patterns. Some important characteristics of a neural network are included in the model, such as different states of activation of a neuron, and excitatory and inhibitory synapses. Our computational model can display different dynamics from fixed-point and limit-cycle to chaotic behaviors similar to different dynamics of a real neuronal population in the brain. Qualitative and quantitative comparisons of the real electroencephalogram data and the CA simulations in the linear and nonlinear domains demonstrated the efficiency of our CA network to simulate the electrical brain activity. Time series from the proposed model display a high-dimensional stochastic behavior that corresponds to the behavior of a healthy brain. Therefore, this model can be used to study biological neuronal populations and provide more insight into their different mechanisms. [ABSTRACT FROM AUTHOR]

We consider the standard neural field equation with an exponential temporal kernel. We analyze the time-independent (static) and time-dependent (dynamic) bifurcations of the equilibrium solution and the emerging spatiotemporal wave patterns. We show that an exponential temporal kernel does not allow static bifurcations such as saddle-node, pitchfork, and in particular, static Turing bifurcations. However, the exponential temporal kernel possesses the important property that it takes into account the finite memory of past activities of neurons, which Green's function does not. Through a dynamic bifurcation analysis, we give explicit bifurcation conditions. Hopf bifurcations lead to temporally non-constant, but spatially constant solutions, but Turing–Hopf bifurcations generate spatially and temporally non-constant solutions, in particular, traveling waves. Bifurcation parameters are the coefficient of the exponential temporal kernel, the transmission speed of neural signals, the time delay rate of synapses, and the ratio of excitatory to inhibitory synaptic weights. [ABSTRACT FROM AUTHOR]

Neurexins play diverse functions as presynaptic organizers in various glutamatergic and GABAergic synapses. However, it remains unknown whether and how neurexins are involved in shaping functional properties of the glycinergic synapses, which mediate prominent inhibition in the brainstem and spinal cord. To address these issues, we examined the role of neurexins in a model glycinergic synapse between the principal neuron in the medial nucleus of the trapezoid body (MNTB) and the principal neuron in the lateral superior olive (LSO) in the auditory brainstem. Combining RNAscope with stereotactic injection of AAV-Cre in the MNTB of neurexin1/2/3 conditional triple knockout mice, we showed that MNTB neurons highly express all isoforms of neurexins although their expression levels vary remarkably. Selective ablation of all neurexins in MNTB neurons not only reduced the amplitude but also altered the kinetics of the glycinergic synaptic transmission at LSO neurons. The synaptic dysfunctions primarily resulted from an impaired Ca2+ sensitivity of release and a loosened coupling between voltage-gated Ca2+ channels and synaptic vesicles. Together, our current findings demonstrate that neurexins are essential in controlling the strength and temporal precision of the glycinergic synapse, which therefore corroborates the role of neurexins as key presynaptic organizers in all major types of fast chemical synapses. [ABSTRACT FROM AUTHOR]

Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell and are found in the brain of all vertebrates. While traditionally viewed as being supportive of neurons, it is increasingly recognized that astrocytes play a more direct and active role in brain function and neural computation. On account of their sensitivity to a host of physiological covariates and ability to modulate neuronal activity and connectivity on slower time scales, astrocytes may be particularly well poised to modulate the dynamics of neural circuits in functionally salient ways. In the current paper, we seek to capture these features via actionable abstractions within computational models of neuron-astrocyte interaction. Specifically, we engage how nested feedback loops of neuron-astrocyte interaction, acting over separated time-scales, may endow astrocytes with the capability to enable learning in context-dependent settings, where fluctuations in task parameters may occur much more slowly than within-task requirements. We pose a general model of neuron-synapse-astrocyte interaction and use formal analysis to characterize how astrocytic modulation may constitute a form of meta-plasticity, altering the ways in which synapses and neurons adapt as a function of time. We then embed this model in a bandit-based reinforcement learning task environment, and show how the presence of time-scale separated astrocytic modulation enables learning over multiple fluctuating contexts. Indeed, these networks learn far more reliably compared to dynamically homogeneous networks and conventional non-network-based bandit algorithms. Our results fuel the notion that neuron-astrocyte interactions in the brain benefit learning over different time-scales and the conveyance of task-relevant contextual information onto circuit dynamics. Author summary: Astrocytes, a non-neuronal cell type, constitute a significant portion of all cells in the brain, yet our understanding of their involvement in neural computation remains limited. In this paper, we use computational modeling to examine how astrocytes may interact with neurons to expand the types of activity and, ultimately, computations that neurons can produce. Two features of astrocytes make them interesting in this context. First, a single astrocyte is able to modulate dozens of neurons and synaptic connections between them. Furthermore, this modulation occurs at slower temporal scales than the neural activity itself, thus meaning that astrocytes could have long-lasting effects on neural activity and connectivity. Our computational models capture both of these features. Using these models, we show that networks with astrocytes can more readily adapt to slowly varying task parameters versus those with neurons alone. We further analyze the models to understand, in mathematical terms, why such effects may arise. Our results shed light on the potential computational role of this common, but enigmatic type of brain cell. [ABSTRACT FROM AUTHOR]

Neuromorphic electronics, inspired by the functions of neurons, have the potential to enable biomimetic communication with cells. Such systems require operation in aqueous environments, generation of sufficient levels of ionic currents for neurostimulation, and plasticity. However, their implementation requires a combination of separate devices, such as sensors, organic synaptic transistors, and stimulation electrodes. Here, a compact neuromorphic synapse that combines photodetection, memory, and neurostimulation functionalities all‐in‐one is presented. The artificial photoreception is facilitated by a photovoltaic device based on cell‐interfacing InP/ZnS quantum dots, which induces photo‐faradaic charge‐transfer mediated plasticity. The device sends excitatory post‐synaptic currents exhibiting paired‐pulse facilitation and post‐tetanic potentiation to the hippocampal neurons via the biohybrid synapse. The electrophysiological recordings indicate modulation of the probability of action potential firing due to biomimetic temporal summation of excitatory post‐synaptic currents. These results pave the way for the development of novel bioinspired neuroprosthetics and soft robotics, and highlight the potential of quantum dots for achieving versatile neuromorphic functionality in aqueous environments. [ABSTRACT FROM AUTHOR]

N-methyl-D-aspartate receptors (NMDARs emerging from GRIN genes) are tetrameric receptors that form diverse channel compositions in neurons, typically consisting of two GluN1 subunits combined with two GluN2(A-D) subunits. During prenatal stages, the predominant channels are di-heteromers with two GluN1 and two GluN2B subunits due to the high abundance of GluN2B subunits. Postnatally, the expression of GluN2A subunits increases, giving rise to additional subtypes, including GluN2A-containing di-heteromers and tri-heteromers with GluN1, GluN2A, and GluN2B subunits. The latter emerge as the major receptor subtype at mature synapses in the hippocampus. Despite extensive research on purely di-heteromeric receptors containing two identical GRIN variants, the impact of a single variant on the function of other channel forms, notably tri-heteromers, is lagging. In this study, we systematically investigated the effects of two de novo GRIN2B variants (G689C and G689S) in pure, mixed di- and tri-heteromers. Our findings reveal that incorporating a single variant in mixed di-heteromers or tri-heteromers exerts a dominant negative effect on glutamate potency, although 'mixed' channels show improved potency compared to pure variant-containing di-heteromers. We show that a single variant within a receptor complex does not impair the response of all receptor subtypes to the positive allosteric modulator pregnenolone-sulfate (PS), whereas spermine completely fails to potentiate tri-heteromers containing GluN2A and -2B-subunits. We examined PS on primary cultured hippocampal neurons transfected with the variants, and observed a positive impact over current amplitudes and synaptic activity. Together, our study supports previous observations showing that mixed di-heteromers exhibit improved glutamate potency and extend these findings towards the exploration of the effect of Loss-of-Function variants over tri-heteromers. Notably, we provide an initial and crucial demonstration of the beneficial effects of GRIN2B-relevant potentiators on tri-heteromers. Our results underscore the significance of studying how different variants affect distinct receptor subtypes, as these effects cannot be inferred solely from observations made on pure di-heteromers. Overall, this study contributes to ongoing efforts to understand the pathophysiology of GRINopathies and provides insights into potential treatment strategies. [ABSTRACT FROM AUTHOR]

Excitatory and inhibitory synapses do not overlap even when formed on one submicron-sized dendritic protrusion. How excitatory and inhibitory postsynaptic cytomatrices or densities (e/iPSDs) are segregated is not understood. Broadly, why membraneless organelles are naturally segregated in cellular subcompartments is unclear. Using biochemical reconstitutions in vitro and in cells, we demonstrate that ePSDs and iPSDs spontaneously segregate into distinct condensed molecular assemblies through phase separation. Tagging iPSD scaffold gephyrin with a PSD-95 intrabody (dissociation constant ~4 nM) leads to mistargeting of gephyrin to ePSD condensates. Unexpectedly, formation of iPSD condensates forces the intrabody-tagged gephyrin out of ePSD condensates. Thus, instead of diffusion-governed spontaneous mixing, demixing is a default process for biomolecules in condensates. Phase separation can generate biomolecular compartmentalization specificities that cannot occur in dilute solutions. [ABSTRACT FROM AUTHOR]

Brain‐inspired electronics with multimodal signal processing have been investigated as the next‐generation semiconductor platforms owing to the limitations of von Neumann architecture, which limits data processing and energy consumption efficiencies. This study demonstrates the molecular reconfiguration of plasticity of artificial synaptic devices with tunable electric conductance based on molecular dynamics at the channel surfaces for realizing chemical multimodality. Carrier transport dynamics are adjusted using the density of trapped carriers for the molecular adsorption of HS in the MoSe2 channel, and the results are consistent with the molecular simulations. In molecular dynamics‐controlled devices, enhanced hysteresis enables the engineering of artificial neuroplasticity, mimicking the neurotransmitter release of biological synapses. Owing to the molecular reconfigurability of MoSe2 devices, the synaptic weights of excitatory and inhibitory synapse modes are significantly enhanced. Thus, this study can potentially contribute to the creation of the next generation of multimodal interfaces and artificial intelligence hardware realization. [ABSTRACT FROM AUTHOR]

Ionic memristors can emulate brain‐like functions of biological synapses for neuromorphic technologies. Apart from the widely studied excitatory‐excitatory and excitatory‐inhibitory synapses, reports on memristors with the inhibitory‐inhibitory synaptic behaviors remain a challenge. Here, the first biaxially inhibited artificial synapse is demonstrated, consisting of a solid electrolyte and conjugated microporous polymers bilayer as neurotransmitter, with the former serving as an ion reservoir and the latter acting as a confined transport. Due to the migration, trapping, and de‐trapping of ions within the nanoslits, the device poses inhibitory synaptic plasticity under both positive and negative stimuli. Remarkably, the artificial synapse is able to maintain a low level of stable nonvolatile memory over a long period of time (≈60 min) after multiple stimuli, with feature‐inferencing/‐training capabilities of neural node in neuromorphic computing. This work paves a reliable strategy for constructing nanochannel ionic memristive materials toward fully inhibitory synaptic devices. [ABSTRACT FROM AUTHOR]

Printed electronics including large‐area sensing, wearables, and bioelectronic systems are often limited to simple circuits and hence it remains a major challenge to efficiently store data and perform computational tasks. Memristors can be considered as ideal candidates for both purposes. Herein, an inkjet‐printed memristor is demonstrated, which can serve as a digital information storage device, or as an artificial synapse for neuromorphic circuits. This is achieved by suitable manipulation of the ion species in the active layer of the device. For digital‐type memristor operation resistive switching is dominated by cation movement after an initial electroforming step. It allows the device to be utilized as non‐volatile digital memristor, which offers high endurance over 12 672 switching cycles and high uniformity at low operating voltages. To use the device as an electroforming‐free, interface‐based, analog‐type memristor, anion migration is exploited which leads to volatile resistive switching. An important figure of merits such as short‐term plasticity with close to biological synapse timescales is demonstrated, for facilitation (10–177 ms), augmentation (10s), and potentiation (35 s). Furthermore, the device is thoroughly studied regarding its metaplasticity for memory formation. Last but not least, the inkjet‐printed artificial synapse shows non‐linear signal integration and low‐frequency filtering capabilities, which renders it a good candidate for neuromorphic computing architectures, such as reservoir computing. [ABSTRACT FROM AUTHOR]

Neuromorphic electronics, inspired by the functions of neurons, have the potential to enable biomimetic communication with cells. Such systems require operation in aqueous environments, generation of sufficient levels of ionic currents for neurostimulation, and plasticity. However, their implementation requires a combination of separate devices, such as sensors, organic synaptic transistors, and stimulation electrodes. Here, a compact neuromorphic synapse that combines photodetection, memory, and neurostimulation functionalities all‐in‐one is presented. The artificial photoreception is facilitated by a photovoltaic device based on cell‐interfacing InP/ZnS quantum dots, which induces photo‐faradaic charge‐transfer mediated plasticity. The device sends excitatory post‐synaptic currents exhibiting paired‐pulse facilitation and post‐tetanic potentiation to the hippocampal neurons via the biohybrid synapse. The electrophysiological recordings indicate modulation of the probability of action potential firing due to biomimetic temporal summation of excitatory post‐synaptic currents. The results pave the way for the development of novel bioinspired neuroprosthetics and soft robotics and highlight the potential of quantum dots for achieving versatile neuromorphic functionality in aqueous environments. [ABSTRACT FROM AUTHOR]

In recent decades, there has been a dramatic rise in the rates of children being born after in utero exposure to drugs of abuse, particularly opioids. Opioids have been shown to have detrimental effects on neurons and glia in the central nervous system (CNS), but the impact of prenatal opioid exposure (POE) on still-developing synaptic circuitry is largely unknown. Astrocytes exert a powerful influence on synaptic development, secreting factors to either promote or inhibit synapse formation and neuronal maturation in the developing CNS. Here, we investigated the effects of the partial µ-opioid receptor agonist buprenorphine on astrocyte synaptogenic signaling and morphological development in cortical cell culture. Acute buprenorphine treatment had no effect on the excitatory synapse number in astrocyte-free neuron cultures. In conditions where neurons shared culture media with astrocytes, buprenorphine attenuated the synaptogenic capabilities of astrocyte-secreted factors. Neurons cultured from drug-naïve mice showed no change in synapses when treated with factors secreted by astrocytes from POE mice. However, this same treatment was synaptogenic when applied to neurons from POE mice, indicating a complex neuroadaptive response in the event of impaired astrocyte signaling. In addition to promoting morphological and connectivity changes in neurons, POE exerted a strong influence on astrocyte development, disrupting their structural maturation and promoting the accumulation of lipid droplets (LDs), suggestive of a maladaptive stress response in the developing CNS. [ABSTRACT FROM AUTHOR]

In all cell types, small EVs, very abundant extracellular vesicles, are generated and accumulated within MVB endocytic cisternae. Upon MVB fusion and exocytosis with the plasma membrane, the EVs are released to the extracellular space. In the central nervous system, the release of neuronal EVs was believed to occur only from the surface of the body and dendrites. About 15 years ago, MVB cisternae and EVs were shown to exist and function at synaptic boutons, the terminals' pre- and post-synaptic structures essential for canonical neurotransmitter release. Recent studies have revealed that synaptic EVs are peculiar in many respects and heterogeneous with respect to other neuronal EVs. The distribution of synaptic EVs and the effect of their specific molecules are found at critical sites of their distribution. The role of synaptic EVs could consist of the modulation of canonical neurotransmitter release or a distinct, non-canonical form of neurotransmission. Additional roles of synaptic EVs are still not completely known. In the future, additional investigations will clarify the role of synaptic EVs in pathology, concerning, for example, circuits, trans-synaptic transmission, diagnosis and the therapy of diseases. [ABSTRACT FROM AUTHOR]

The optical synaptic device is a novel nonvolatile memory device that combines optical sensing function with synaptic plasticity to simulate the basic biomimetic behaviors of the human visual system. It shows a great potential application in artificial vision. However, most of the current optical synaptic devices are either structurally special or complex, which are quite difficult to prepare especially for mass production. In this work, a submillimeter single‐crystal Bi2O2Se flake with selenium vacancies (Bi2O2Se‐VSe) grown by physical vapor deposition is presented. Upon the Bi2O2Se‐VSe flake, an optical synaptic device is designed with the persistent photoconductivity (PPC) effect induced by selenium vacancies. The device can simulate the biological synapses with an obvious synaptic plasticity. Moreover, an artificial vision system consisting of a 3 × 4 array of optical synaptic devices has been fabricated. The intensity of the image pattern can keep at a high memory level of 54.64% and 19.31% for 532 and 1060 nm waiting for 400 s after illumination of 100 s, which demonstrates the devices exhibit excellent image sensing, learning, and memory storage. This work opens up a new route for fabricating the optical synaptic device with a simple structure and provides new ideas for studying artificial vision systems. [ABSTRACT FROM AUTHOR]

Mounting evidence indicates that a physiological function of amyloid-β (Aβ) is to mediate neural activity-dependent homeostatic and competitive synaptic plasticity in the brain. I have previously summarized the lines of evidence supporting this hypothesis and highlighted the similarities between Aβ and anti-microbial peptides in mediating cell/synapse competition. In cell competition, anti-microbial peptides deploy a multitude of mechanisms to ensure both self-protection and competitor elimination. Here I review recent studies showing that similar mechanisms are at play in Aβ-mediated synapse competition and perturbations in these mechanisms underpin Alzheimer's disease (AD). Specifically, I discuss evidence that Aβ and ApoE, two crucial players in AD, co-operate in the regulation of synapse competition. Glial ApoE promotes self-protection by increasing the production of trophic monomeric Aβ and inhibiting its assembly into toxic oligomers. Conversely, Aβ oligomers, once assembled, promote the elimination of competitor synapses via direct toxic activity and amplification of "eat-me" signals promoting the elimination of weak synapses. I further summarize evidence that neuronal ApoE may be part of a gene regulatory network that normally promotes competitive plasticity, explaining the selective vulnerability of ApoE expressing neurons in AD brains. Lastly, I discuss evidence that sleep may be key to Aβ-orchestrated plasticity, in which sleep is not only induced by Aβ but is also required for Aβ-mediated plasticity, underlining the link between sleep and AD. Together, these results strongly argue that AD is a disease of competitive synaptic plasticity gone awry, a novel perspective that may promote AD research. [ABSTRACT FROM AUTHOR]

The development of large-scale manufacturing and cost-saving artificial synaptic devices is of great significance for the realization of hardware neural networks. In this work, the In2O3 synaptic transistors with boron ion-doped ZrOx as the dielectric layer were fabricated by the solution process. The large counterclockwise hysteresis indicates that the nonvolatile memory capacity of the synaptic transistors was improved by boron ions doping. Typical synaptic functions including excitatory postsynaptic current, paired pulse facilitation, and high-pass filtering properties were simulated by applying gate bias. The multilevel channel conductance can be modulated by successive positive and negative pulses. Additionally, small nonlinearities were shown in the potentiation and depression processes, and high accuracies of 92.0% (small digits) and 93.8% (large digits) were obtained in digital recognition training. This study indicates that oxide-gated synaptic transistors based on the boron ions doping strategy show great potential for neuromorphic applications. [ABSTRACT FROM AUTHOR]

Spatio-temporal activity patterns have been observed in a variety of brain areas in spontaneous activity, prior to or during action, or in response to stimuli. Biological mechanisms endowing neurons with the ability to distinguish between different sequences remain largely unknown. Learning sequences of spikes raises multiple challenges, such as maintaining in memory spike history and discriminating partially overlapping sequences. Here, we show that anti-Hebbian spike-timing dependent plasticity (STDP), as observed at cortico-striatal synapses, can naturally lead to learning spike sequences. We design a spiking model of the striatal output neuron receiving spike patterns defined as sequential input from a fixed set of cortical neurons. We use a simple synaptic plasticity rule that combines anti-Hebbian STDP and non-associative potentiation for a subset of the presented patterns called rewarded patterns. We study the ability of striatal output neurons to discriminate rewarded from non-rewarded patterns by firing only after the presentation of a rewarded pattern. In particular, we show that two biological properties of striatal networks, spiking latency and collateral inhibition, contribute to an increase in accuracy, by allowing a better discrimination of partially overlapping sequences. These results suggest that anti-Hebbian STDP may serve as a biological substrate for learning sequences of spikes. Mathematical models and computer simulations suggest that anti-Hebbian STDP, the type of plasticity observed between cortex and striatum, could support the learning of sequences in the brain. [ABSTRACT FROM AUTHOR]

Integrated multifunctionality in visual information processing is crucial in the artificial intelligence era. Compared to the parallel human vision system, current bionic vision devices exhibit a complex structure with single functionality, challenging intelligent processing and integration. Here, a multisensory artificial synapse with a crossbar structure comprising graphene/α‐In2Se3/graphene layers is demonstrated, merging sensing, memory, and computing while mimicking various synaptic properties. The Schottky barrier height is modulated by the polarization of ferroelectric semiconductor α‐In2Se3, enabling reconfigurable device conductance and photoresponsivity. This conductance emulates synaptic short‐term and long‐term plasticity through electrical pulse modulation, boasting a rapid 40 ns programming speed. The device also exhibits linearly regulated photoresponsivity under illumination, with synaptic plasticity from optical pulses. The fusion of electronic and optoelectronic devices enables both image front‐end processing and advanced post‐processing. In‐sensor front‐end processing enhances subsequent processing efficiency, with pattern recognition accuracy reaching 97%. This design fosters the advancement of multisensory and highly integrated neuromorphic vision systems. [ABSTRACT FROM AUTHOR]