Synaptic Element Research Articles

Changes in Neuroimmunological Synapses During Cerebral Ischemia.

The direct interplay between the immune and nervous systems is now well established. Within the brain, these interactions take place between neurons and resident glial cells, i.e., microglia and astrocytes, or infiltrating immune cells, influenced by systemic factors. A special form of physical cell-cell interactions is the so-called "neuroimmunological (NI) synapse." There is compelling evidence that the same signaling pathways that regulate inflammatory responses to injury or ischemia also play potent roles in brain development, plasticity, and function. Proper synaptic wiring is as important during development as it is during disease states, as it is necessary for activity-dependent refinement of neuronal circuits. Since the process of forming synaptic connections in the brain is highly dynamic, with constant changes in strength and connectivity, the immune component is perfectly suited for the regulatory task as it is in constant turnover. Many cellular and molecular players in this interaction remain to be uncovered, especially in pathological states. In this review, we discuss and propose possible communication hubs between components of the adaptive and innate immune systems and the synaptic element in ischemic stroke pathology.

EZH2-dependent epigenetic reprogramming in the central nucleus of amygdala regulates adult anxiety in both sexes after adolescent alcohol exposure

Alcohol use and anxiety disorders occur in both males and females, but despite sharing similar presentation and classical symptoms, the prevalence of alcohol use disorder (AUD) is lower in females. While anxiety is a symptom and comorbidity shared by both sexes, the common underlying mechanism that leads to AUD and the subsequent development of anxiety is still understudied. Using a rodent model of adolescent intermittent ethanol (AIE) exposure in both sexes, we investigated the epigenetic mechanism mediated by enhancer of zeste 2 (EZH2), a histone methyltransferase, in regulating both the expression of activity-regulated cytoskeleton-associated protein (Arc) and an anxiety-like phenotype in adulthood. Here, we report that EZH2 protein levels were significantly higher in PKC-δ positive GABAergic neurons in the central nucleus of amygdala (CeA) of adult male and female rats after AIE. Reducing protein and mRNA levels of EZH2 using siRNA infusion in the CeA prevented AIE-induced anxiety-like behavior, increased H3K27me3, decreased H3K27ac at the Arc synaptic activity response element (SARE) site, and restored deficits in Arc mRNA and protein expression in both male and female adult rats. Our data indicate that an EZH2-mediated epigenetic mechanism in the CeA plays an important role in regulating anxiety-like behavior and Arc expression after AIE in both male and female rats in adulthood. This study suggests that EZH2 may serve as a tractable drug target for the treatment of adult psychopathology after adolescent alcohol exposure.

Brain-inspired computing with fluidic iontronic nanochannels

The brain's remarkable and efficient information processing capability is driving research into brain-inspired (neuromorphic) computing paradigms. Artificial aqueous ion channels are emerging as an exciting platform for neuromorphic computing, representing a departure from conventional solid-state devices by directly mimicking the brain's fluidic ion transport. Supported by a quantitative theoretical model, we present easy-to-fabricate tapered microchannels that embed a conducting network of fluidic nanochannels between a colloidal structure. Due to transient salt concentration polarization, our devices are volatile memristors (memory resistors) that are remarkably stable. The voltage-driven net salt flux and accumulation, that underpin the concentration polarization, surprisingly combine into a diffusionlike quadratic dependence of the memory retention time on the channel length, allowing channel design for a specific timescale. We implement our device as a synaptic element for neuromorphic reservoir computing. Individual channels distinguish various time series, that together represent (handwritten) numbers, for subsequent in silico classification with a simple readout function. Our results represent a significant step toward realizing the promise of fluidic ion channels as a platform to emulate the rich aqueous dynamics of the brain.

Programming Techniques of Resistive Random-Access Memory Devices for Neuromorphic Computing

Neuromorphic computing offers a promising solution to overcome the von Neumann bottleneck, where the separation between the memory and the processor poses increasing limitations of latency and power consumption. For this purpose, a device with analog switching for weight update is necessary to implement neuromorphic applications. In the diversity of emerging devices postulated as synaptic elements in neural networks, RRAM emerges as a standout candidate for its ability to tune its resistance. The learning accuracy of a neural network is directly related to the linearity and symmetry of the weight update behavior of the synaptic element. However, it is challenging to obtain such a linear and symmetrical behavior with RRAM devices. Thus, extensive research is currently devoted at different levels, from material to device engineering, to improve the linearity and symmetry of the conductance update of RRAM devices. In this work, the experimental results based on different programming pulse conditions of RRAM devices are presented, considering both voltage and current pulses. Their suitability for application as analog RRAM-based synaptic devices for neuromorphic computing is analyzed by computing an asymmetric nonlinearity factor.

Chemogenetic inhibition of subicular seizure-activated neurons alleviates cognitive deficit in male mouse epilepsy model.

Cognitive deficit is a common comorbidity in temporal lobe epilepsy (TLE) and is not well controlled by current therapeutics. How epileptic seizure affects cognitive performance remains largely unclear. In this study we investigated the role of subicular seizure-activated neurons in cognitive impairment in TLE. A bipolar electrode was implanted into hippocampal CA3 in male mice for kindling stimulation and EEG recording; a special promoter with enhanced synaptic activity-responsive element (E-SARE) was used to label seizure-activated neurons in the subiculum; the activity of subicular seizure-activated neurons was manipulated using chemogenetic approach; cognitive function was assessed in object location memory (OLM) and novel object recognition (NOR) tasks. We showed that chemogenetic inhibition of subicular seizure-activated neurons (mainly CaMKIIα+ glutamatergic neurons) alleviated seizure generalization and improved cognitive performance, but inhibition of seizure-activated GABAergic interneurons had no effect on seizure and cognition. For comparison, inhibition of the whole subicular CaMKIIα+ neuron impaired cognitive function in naïve mice in basal condition. Notably, chemogenetic inhibition of subicular seizure-activated neurons enhanced the recruitment of cognition-responsive c-fos+ neurons via increasing neural excitability during cognition tasks. Our results demonstrate that subicular seizure-activated neurons contribute to cognitive impairment in TLE, suggesting seizure-activated neurons as the potential therapeutic target to alleviate cognitive impairment in TLE.

A Ti‐Doped Chemical Vapor Deposition Diamond Device as Artificial Synapse for Neuromorphic Applications

AbstractThe great demand of multifunctional portable electronic products in daily life and the need of a large integration of memories with logic devices and sensors, have increased the interest in the identification of suitable materials for neuromorphic computing applications. Major innovations in this direction have been achieved by exploring materials belonging to different fields of applications and taking advantage of already consolidated deposition methods. Despite the great interest in the field and the large use in complementary applications such as sensing electrodes, neural and cellular interfaces, the use of diamond‐like materials in neuromorphic applications is still limited to a few examples. Herein, the development of a synaptic element based on high‐quality polycrystalline diamond layers containing Ti inclusions showing a marked and reproducible resistance switching behavior is reported. Realized by means of a hybrid chemical vapor deposition‐powder flowing technique, this titanium doped diamond shows a 3D polycrystalline organization that is characterized by globular grains of a few microns. The coupling of Raman spectroscopy, X‐ray photoelectron spectroscopy, and X‐ray diffraction analyses confirms the good quality of diamond phase and convincingly points out the inclusion of the titanium species within the diamond lattice.

Iontronic analog of synaptic plasticity: Hydrogel-based ionic diode with chemical precipitation and dissolution

In this study, an aqueous nonlinear synaptic element showing plasticity behavior is developed, which is based on the chemical processes in an ionic diode. The device is simple, fully ionic, and easily configurable, requiring only two terminals-for input and output-similar to biological synapses. The key processes realizing the plasticity features are chemical precipitation and dissolution, which occur at forward- or reverse-biased ionic diode junctions in appropriate reservoir electrolytes. Given that the precipitate acts as a physical barrier in the circuit, the above processes change the diode conductivity, which can be interpreted as adjusting "synaptic weight" of the system. By varying the operating conditions, we first demonstrate the four types of plasticity that can be found in biological system: long-term potentiation/depression and short-term potentiation/depression. The plasticity of the proposed iontronic device has characteristics similar to those of neural synapses. To demonstrate its potential use in comparatively complex information processing, we develop a precipitation-based iontronic synapse (PIS) capable of both potentiation and depression. Finally, we show that the postsynaptic signals from the multiple excitatory or inhibitory PISs can be integrated into the total "dendritic" current, which is a function of time and input history, as in actual hippocampal neural circuits.

Physical Compact Model for Three‐Terminal SONOS Synaptic Circuit Element

Neuromorphic Circuit Design In article number 2200070, R. Stanley Williams and co-workers present SONOS three-terminal synaptic circuit element, known as Flash memory, which is compact-modeled for neuromorphic applications, mimicking a brain. Similar to most two-terminal non-volatile memories such as RRAMs presenting memristors, the compact model of three-terminal non-volatile memory reveals the memristor on the third terminal. The physical compact model opens up opportunities at the intersection of the manufacturable non-volatile memory and the artificial brain.

A non-invasive system to monitor in vivo neural graft activity after spinal cord injury

Expectations for neural stem/progenitor cell (NS/PC) transplantation as a treatment for spinal cord injury (SCI) are increasing. However, whether and how grafted cells are incorporated into the host neural circuit and contribute to motor function recovery remain unknown. The aim of this project was to establish a novel non-invasive in vivo imaging system to visualize the activity of neural grafts by which we can simultaneously demonstrate the circuit-level integration between the graft and host and the contribution of graft neuronal activity to host behaviour. We introduced Akaluc, a newly engineered luciferase, under the control of enhanced synaptic activity-responsive element (E-SARE), a potent neuronal activity-dependent synthetic promoter, into NS/PCs and engrafted the cells into SCI model mice. Through the use of this system, we found that the activity of grafted cells was integrated with host behaviour and driven by host neural circuit inputs. This non-invasive system is expected to help elucidate the therapeutic mechanism of cell transplantation treatment for SCI.

Physical Compact Model for Three‐Terminal SONOS Synaptic Circuit Element

A well‐posed physics‐based compact model for a three‐terminal silicon–oxide–nitride–oxide–silicon (SONOS) synaptic circuit element is presented for use by neuromorphic circuit/system engineers. Based on technology computer aided design (TCAD) simulations of a SONOS device, the model contains a nonvolatile memristor with the state variable QM representing the memristor charge under the gate of the three‐terminal element. By incorporating the exponential dependence of the memristance on QM and the applied bias V for the gate, the compact model agrees quantitatively with the results from TCAD simulations as well as experimental measurements for the drain current. The compact model is implemented through VerilogA in the circuit simulation package Cadence Spectre and reproduces the experimental training behavior for the source–drain conductance of a SONOS device after applying writing pulses ranging from −12 V to +11 V, with an accuracy higher than 90%.

Targeted epigenomic editing ameliorates adult anxiety and excessive drinking after adolescent alcohol exposure.

Adolescent binge drinking is a major risk factor for psychiatric disorders later in life including alcohol use disorder. Adolescent alcohol exposure induces epigenetic reprogramming at the enhancer region of the activity-regulated cytoskeleton-associated protein (Arc) immediate-early gene, known as synaptic activity response element (SARE), and decreases Arc expression in the amygdala of both rodents and humans. The causal role of amygdalar epigenomic regulation at Arc SARE in adult anxiety and drinking after adolescent alcohol exposure is unknown. Here, we show that dCas9-P300 increases histone acetylation at the Arc SARE and normalizes deficits in Arc expression, leading to attenuation of adult anxiety and excessive alcohol drinking in a rat model of adolescent alcohol exposure. Conversely, dCas9-KRAB increases repressive histone methylation at the Arc SARE, decreases Arc expression, and produces anxiety and alcohol drinking in control rats. These results demonstrate that epigenomic editing in the amygdala can ameliorate adult psychopathology after adolescent alcohol exposure.

Early stress-induced impaired microglial pruning of excitatory synapses on immature CRH-expressing neurons provokes aberrant adult stress responses.

SUMMARYSeveral mental illnesses, characterized by aberrant stress reactivity, often arise after early-life adversity (ELA). However, it is unclear how ELA affects stress-related brain circuit maturation, provoking these enduring vulnerabilities. We find that ELA increases functional excitatory synapses onto stress-sensitive hypothalamic corticotropin-releasing hormone (CRH)-expressing neurons, resulting from disrupted developmental synapse pruning by adjacent microglia. Microglial process dynamics and synaptic element engulfment were attenuated in ELA mice, associated with deficient signaling of the microglial phagocytic receptor MerTK. Accordingly, selective chronic chemogenetic activation of ELA microglia increased microglial process dynamics and reduced excitatory synapse density to control levels. Notably, selective early-life activation of ELA microglia normalized adult acute and chronic stress responses, including stress-induced hormone secretion and behavioral threat responses, as well as chronic adrenal hypertrophy of ELA mice. Thus, microglial actions during development are powerful contributors to mechanisms by which ELA sculpts the connectivity of stress-regulating neurons, promoting vulnerability to stress and stress-related mental illnesses.

3D AND-Type Ferroelectric Transistors for Compute-in-Memory and the Variability Analysis

Three-dimensional AND-type architecture (3D AND) based on ferroelectric field-effect transistors (FeFETs) is proposed for the vector-matrix multiplication (VMM) of compute-in-memory paradigm. By leveraging a vertical string as one synaptic element, multibit weight with controllable linearity is realized due to parallel connection of cells in a string. Multibit input is also feasible with appropriate word line (WL) biasing. To guarantee the deep neural network performance, the device-level conductance distribution should be tightened. The phase variation of ferroelectric layer is regarded as a primary variation source, and its impact on the inference accuracy is examined.

Editorial: Neuromorphic Memristive Computation: Where Memristor-Based Designs Meet Artificial Intelligence Applications

Intelligence Applications 16The definition of a memristor was introduced by Professor L. O. Chua in 1971. By generalizing the 17 definition of a memristor, memristive devices and systems were also proposed. Since the real 18 memristor was implemented at Hewlett-Packard Labs, there has been an increasing interest in 19 memristor and its applications. Memristor is a kind of synaptic element with nanometer size and continuously variable memristance. 68The bridge synaptic circuit constructed by the memristor has a simple structure and precise control. In 69 practice, because of the non-linear characteristics of memristor, it is not easy to control synaptic circuit 70 and errors in weights appear. Therefore, a novel memristor synaptic circuit is proposed by Wang et al., 71 named the dual-mode memristor bridge synaptic neural network. The proposed method can make the 72 weights more linear by controlling the input voltages and make the outputs more linear by using 73 symmetrical positive and negative pulses. As a result, the proposed synaptic circuit is easier to be 74 controlled. The numerical simulations are conducted and verify the feasibility. Furthermore, the 75 simulation experiments are conducted for edge extraction of grayscale birds' images in the airport for 76 bird recognition applied for the bird repelling applications. 77

Weight adjustable photonic synapse by nonlinear gain in a vertical cavity semiconductor optical amplifier

In this paper, we report a high-speed and tunable photonic synaptic element based on a vertical cavity semiconductor optical amplifier (VCSOA) operating with short (150 ps-long) and low-energy (μW peak power) light pulses. By exploiting nonlinear gain properties of VCSOAs when subject to external optical injection, our system permits full weight tunability of sub-ns input light pulses, just by varying the VCSOA's applied bias current. Not only is the VCSOA-based synapse able to adjust the strength of incoming optical pulses, but it can also provide gain (applied weight factors >1). Moreover, we show that this simple approach permits dynamical weight tuning at high-speed (ns rates) with up to an 11.6-bit precision. These results are realized with commercially sourced, inexpensive vertical cavity surface emitting lasers, operating at the key telecom wavelengths of 1300 and 1550 nm and hence making our approach compatible with optical network and data center technologies. This VCSOA-based system, therefore, offers a hardware friendly, low-energy, and high-speed solution for photonic synaptic links with high potential for use in future neuromorphic photonic systems.

Impact of electrolyte density on synaptic characteristics of oxygen-based ionic synaptic transistor

An oxygen-based ionic synaptic transistor (O-IST) is a promising synaptic element for neuromorphic computing. In this study, we demonstrated that the density of the electrolyte plays a key role in achieving excellent synaptic characteristics in an O-IST. In a Pr0.7Ca0.3MnO3-based O-IST, we precisely controlled the density of the HfOx electrolyte and found that a low-density electrolyte could improve the ion mobility. Owing to the improved ion mobility and controlled ion migration, we demonstrated that excellent synaptic characteristics, such as a wide dynamic range, linear weight update, low operating voltage operations, and stable cyclic operation, were achieved. Finally, we confirmed an improved pattern recognition accuracy using an O-IST with an HfOx electrolyte of optimal density.

Dual-Mode Memristor Synaptic Circuit Design and Application in Image Processing

Memristor is a kind of synaptic element with nanometer size and continuously variable memristance. The bridge synaptic circuit constructed by the memristor has a simple structure and precise control. In practice, because of the non-linear characteristics of memristor, it is not easy to control synaptic circuit and errors in weights appear. Therefore, a novel memristor synaptic circuit is proposed in this paper, called the dual-mode memristor bridge synaptic neural network. The proposed method can make the weights more linear by controlling the input voltages and make the outputs more linear by using symmetrical positive and negative pulses. Therefore, the proposed synaptic circuit is easier to be controlled. In this paper, the numerical simulations are conducted and verify the feasibility. Furthermore, the simulation experiments are conducted for edge extraction of grayscale birds’ images in the airport for bird recognition applied for the bird repelling applications.

Learning to Approximate Functions Using Nb-Doped SrTiO3 Memristors.

Memristors have attracted interest as neuromorphic computation elements because they show promise in enabling efficient hardware implementations of artificial neurons and synapses. We performed measurements on interface-type memristors to validate their use in neuromorphic hardware. Specifically, we utilized Nb-doped SrTiO3 memristors as synapses in a simulated neural network by arranging them into differential synaptic pairs, with the weight of the connection given by the difference in normalized conductance values between the two paired memristors. This network learned to represent functions through a training process based on a novel supervised learning algorithm, during which discrete voltage pulses were applied to one of the two memristors in each pair. To simulate the fact that both the initial state of the physical memristive devices and the impact of each voltage pulse are unknown we injected noise into the simulation. Nevertheless, discrete updates based on local knowledge were shown to result in robust learning performance. Using this class of memristive devices as the synaptic weight element in a spiking neural network yields, to our knowledge, one of the first models of this kind, capable of learning to be a universal function approximator, and strongly suggests the suitability of these memristors for usage in future computing platforms.

Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics

A graphdiyne-based artificial synapse (GAS), exhibiting intrinsic short-term plasticity, has been proposed to mimic biological signal transmission behavior. The impulse response of the GAS has been reduced to several millivolts with competitive femtowatt-level consumption, exceeding the biological level by orders of magnitude. Most importantly, the GAS is capable of parallelly processing signals transmitted from multiple pre-neurons and therefore realizing dynamic logic and spatiotemporal rules. It is also found that the GAS is thermally stable (at 353 K) and environmentally stable (in a relative humidity up to 35%). Our artificial efferent nerve, connecting the GAS with artificial muscles, has been demonstrated to complete the information integration of pre-neurons and the information output of motor neurons, which is advantageous for coalescing multiple sensory feedbacks and reacting to events. Our synaptic element has potential applications in bioinspired peripheral nervous systems of soft electronics, neurorobotics, and biohybrid systems of brain–computer interfaces.

Oxide-Based Electrolyte-Gated Transistors for Spatiotemporal Information Processing.

Spiking neural networks (SNNs) sharing large similarity with biological nervous systems are promising to process spatiotemporal information and can provide highly time- and energy-efficient computational paradigms for the Internet-of-Things and edge computing. Nonvolatile electrolyte-gated transistors (EGTs) provide prominent analog switching performance, the most critical feature of synaptic element, and have been recently demonstrated as a promising synaptic device. However, high performance, large-scale EGT arrays, and EGT application for spatiotemporal information processing in an SNN are yet to be demonstrated. Here, an oxide-based EGT employing amorphous Nb2 O5 and Lix SiO2 is introduced as the channel and electrolyte gate materials, respectively, and integrated into a 32 × 32 EGT array. The engineered EGTs show a quasi-linear update, good endurance (106 ) and retention, a high switching speed of 100ns, ultralow readout conductance (<100 nS), and ultralow areal switching energy density (20 fJ µm-2 ). The prominent analog switching performance is leveraged for hardware implementation of an SNN with the capability of spatiotemporal information processing, where spike sequences with different timings are able to be efficiently learned and recognized by the EGT array. Finally, this EGT-based spatiotemporal information processing is deployed to detect moving orientation in a tactile sensing system. These results provide an insight into oxide-based EGT devices for energy-efficient neuromorphic computing to support edge application.