Spike Characteristics Research Articles

Stress-induced artificial neuron spiking in diffusive memristors

Diffusive memristors owing to their ability to produce current spiking when a constant or slowly changing voltage is applied are competitive candidates for development of artificial electronic neurons. These artificial neurons can be integrated into various prospective autonomous and robotic systems as sensors, e.g. ones implementing object grasping and classification. We report here Ag nanoparticle-based diffusive memristor prepared on a flexible polyethylene terephthalate substrate in which the electric spiking behaviour was induced by the electric voltage under an additional stimulus of external mechanical impact. By changing the magnitude and frequency of the mechanical impact, we are able to manipulate the spiking response of our artificial neuron. This functionality to control the spiking characteristics paves a pathway for the development of touch-perception sensors that can convert local pressure into electrical spikes for further processing in neural networks. We have proposed a mathematical model which captures the operation principle of the fabricated memristive sensors and qualitatively describes the measured spiking behaviour. Employing such flexible diffusive memristors that can directly translate tactile information into spikes, similar to force and pressure sensors, could offer substantial benefits for various applications in robotics.

Spike by spike frequency analysis of amperometry traces provides statistical validation of observations in the time domain

Amperometry is a commonly used electrochemical method for studying the process of exocytosis in real-time. Given the high precision of recording that amperometry procedures offer, the volume of data generated can span over several hundreds of megabytes to a few gigabytes and therefore necessitates systematic and reproducible methods for analysis. Though the spike characteristics of amperometry traces in the time domain hold information about the dynamics of exocytosis, these biochemical signals are, more often than not, characterized by time-varying signal properties. Such signals with time-variant properties may occur at different frequencies and therefore analyzing them in the frequency domain may provide statistical validation for observations already established in the time domain. This necessitates the use of time-variant, frequency-selective signal processing methods as well, which can adeptly quantify the dominant or mean frequencies in the signal. The Fast Fourier Transform (FFT) is a well-established computational tool that is commonly used to find the frequency components of a signal buried in noise. In this work, we outline a method for spike-based frequency analysis of amperometry traces using FFT that also provides statistical validation of observations on spike characteristics in the time domain. We demonstrate the method by utilizing simulated signals and by subsequently testing it on diverse amperometry datasets generated from different experiments with various chemical stimulations. To our knowledge, this is the first fully automated open-source tool available dedicated to the analysis of spikes extracted from amperometry signals in the frequency domain.

Evaluation of the Spike Diversity of Seven Hexaploid Wheat Species and an Artificial Amphidiploid Using a Quadrangle Model Obtained from 2D Images

The spike shape and morphometric characteristics are among the key characteristics of cultivated cereals, being associated with their productivity. These traits are often used for the plant taxonomy and authenticity of hexaploid wheat species. Manual measurement of spike characteristics is tedious and not precise. Recently, the authors of this study developed a method for wheat spike morphometry utilizing 2D image analysis. Here, this method is applied to study variations in spike size and shape for 190 plants of seven hexaploid (2n = 6x = 42) species and one artificial amphidiploid of wheat. Five manually estimated spike traits and 26 traits obtained from digital image analysis were analyzed. Image-based traits describe the characteristics of the base, center and apex of the spike and common parameters (circularity, roundness, perimeter, etc.). Estimates of similar traits by manual measurement and image analysis were shown to be highly correlated, suggesting the practical importance of digital spike phenotyping. The utility of spike traits for classification into types (spelt, normal and compact) and species or amphidiploid is shown. It is also demonstrated that the estimates obtained made it possible to identify the spike characteristics differing significantly between species or between accessions within the same species. The present work suggests the usefulness of wheat spike shape analysis using an approach based on characteristics obtained by digital image analysis.

EFFECT OF SOIL CONDITIONERS APPLIED TO SEED ON GRAIN YIELD AND YIELD CHARACTERISTICS IN WHEAT

This study, researching the effect of seed treatment with liquid soil conditioners on yield properties of bread wheat varieties, was completed in 2017 and 2018 in the experimental field of Tekirdag Namik Kemal University Faculty of Agriculture, Department of Field Crops. The experiments were conducted with 3 bread wheat varieties and 4 soil amendments (control + 3 different liquid soil amendments) in 3 repetitions. Flamura 85, Selimiye and Esperia bread wheat varieties, commonly sown in the Thrace region were used as experimental material. In the experiment, 4 different treatments including 3 different soil regulators and 1 control (T1: Control; T2: 13-5-8+glycine betaine; T3: 15% organic matter, 15% humic and fulvic acid+0.03% potassium and T4: 25% organic matter + 65% humic acid + 6% potassium (T4) were made. Seeds treated with a spray and then dried were sown as split plot experimental design. In the study, the variations in the plant height (PH), spike length (SL), number of spikelets per spike (NSS), number of grains per spike (NGS), grain weight per spike (GWS), spike fertility index (SFI), harvest index (HI) and grain yield (GY) parameters were investigated for the bread wheat varieties. According to the research results, all soil conditioners applied to seeds were determined to affect the investigated characters at a statistically significant level. T4 treatment caused clear increases in the SL, NSS, NGS, GWS and GY parameters. For the PH parameter, T3 treatment caused a significant increase, while for the SFI parameter, T2 treatment caused a significant increase. For the HI parameter, treatments T2 and T3 had the highest effect. T4 treatment caused an increase in plant height for the Flamura 85 variety, reduced the plant height of the Selimiye variety and had no statistically significant effect on the Esperia variety. Spike characteristics like SL, NSS and NGS increased compared to controls with all soil conditioner treatments, while parameters like GWS and BFI differed according to variety. For the HI parameter, the T2 treatment of the Selimiye and Esperia varieties and the T3 treatment of Flamura 85 variety provided highest results. Grain yield, the most important parameter for wheat, provided the highest results in different soil conditioner treatments depending on the variety; the most significant increases were observed in Esperia variety with T2 treatment and in Flamura 85 variety with T3 treatment.

Nanoimpact Electrochemistry for Unraveling the Reactivity of Diffusion Limited Enzymes

The ultimate challenge of modern electroanalytical chemistry is to reach a single-molecule level of detection. In this framework, nanoimpact electrochemistry (NIE) has been recently developed. It allows studying at high throughput the reactivity of small entities such as nanoparticles, cells, or proteins, that collide on a biased ultramicroelectrode (UME) inside a solution or suspension.1 The collisions cause disturbances in the recorded chronoamperogram such as current spikes or current steps.2 Single enzyme electrochemistry (SEE) can contribute to uncovering effects of dynamics and non-equilibrium behavior of individual enzyme molecules on catalysis, which are hidden in measurements on ensembles due to averaging of the measured parameters over the entire molecular population. However, NIE of smaller entities such as enzymes, is very challenging due to the low sensitivity of the state-of-the-art electrochemical equipment, and most single enzyme techniques are currently based on fluorescence.3 One strategy to overcome this issue is the amplification of the current produced by a single enzyme molecule by fast catalysis: the single enzyme converts several substrate molecules per second consuming several electrons leading measurable current. Some of us reported the implementation of this strategy for the first time, designing a single-entity electrochemical experiment that allowed the detection of catalytic currents from single laccase molecules.4 Soon after the initial SEE report in 2016, a similar approach was applied for investigations of catalase,5 an enzyme that breaks down H2O2 to O2 and H2O with diffusion limited kinetics. However, the unequivocal attribution of current disturbances to the reduction of O2 (produced locally by enzyme) on the electrode during enzyme-electrode collisions remains a challenge. We will address this challenge based on the analysis of SEE experimental data under various conditions, on protein film voltammetry (PFV) studies on rotating disk electrode (RDE), and on simulations. The collected data from the SEE, that is the current spike characteristics in each set of conditions, are statistically analyzed and discussed. PFV studies are used to evaluate basic thermodynamic and kinetic parameters of the system. Finally, we have built a model using Comsol Multiphysics. To the best of our knowledge, this is the first model reported so far that can satisfactorily reproduce the experimental SEE data, using experimentally evaluated parameters as input. The developed methodology is the succesfully implemented to the study of superoxide dismutase.6

The addition of Psathyrostachys Huashanica Keng 6Ns large segment chromosomes has positive impact on stripe rust resistance and plant spikelet number of common wheat

BackgroundDeveloping novel germplasm by using wheat wild related species is an effective way to rebuild the wheat resource bank. The Psathyrostachys huashanica Keng (P. huashanica, 2n = 2x = 14, NsNs) is regarded as a superior species to improve wheat breeding because of its multi-resistance, early maturation and numerous tiller traits. Introducing genetic components of P. huashanica into the common wheat background is the most important step in achieving the effective use. Therefore, the cytogenetic characterization and influence of the introgressed P. huashanica large segment chromosomes in the wheat background is necessary to be explored.ResultsIn this study, we characterized a novel derived line, named D88-2a, a progeny of the former characterized wheat-P. huashanica partial amphiploid line H8911 (2n = 7x = 49, AABBDDNs). Cytological identification showed that the chromosomal composition of D88-2a was 2n = 44 = 22II, indicating the addition of exogenous chromosomes. Genomic in situ hybridization demonstrated that the supernumerary chromosomes were a pair of homologues from the P. huashanica and could be stably inherited in the common wheat background. Molecular markers and 15 K SNP array indicated that the additional chromosomes were derived from the sixth homoeologous group (i.e., 6Ns) of P. huashanica. Based on the distribution of the heterozygous single-nucleotide polymorphism sites and fluorescence in situ hybridization karyotype of each chromosome, this pair of additional chromosomes was confirmed as P. huashanica 6Ns large segment chromosomes, which contained the entire short arm and the proximal centromere portion of the long arm. In terms of the agronomic traits, the addition line D88-2a exhibited enhanced stripe rust resistance, improved spike characteristics and increased protein content than its wheat parent line 7182.ConclusionsThe new wheat germplasm D88-2a is a novel cytogenetically stable wheat-P. huashanica 6Ns large segment addition line, and the introgressed large segment alien chromosome has positive impact on plant spikelet number and stripe rust resistance. Thus, this germplasm can be used for genetic improvement of cultivated wheat and the study of functional alien chromosome segment.

Generalized Bio-Plausible Neuron Model with Refractoriness, Frequency Adaptation and Dynamic Threshold

Real biological neurons are dynamical systems that can process a nonlinear, arbitrary input signal with a dynamic threshold voltage, exhibit refractory time, and have a smooth [Formula: see text]–[Formula: see text] curve. This paper presents a generalized bio-plausible neuron model with refractoriness, frequency adaptation, and dynamic threshold. The spiking characteristics of the generalized neuron are enhanced with less circuitry and fewer parameters compared to older circuits. The proposed silicon neuron can generate all known spiking behaviors, just like a biological neuron. The circuit is designed using a 180[Formula: see text]nm standard CMOS process. The entire design uses 22 transistors with a total power consumption of only 2[Formula: see text]nW for a 1[Formula: see text]nA step current at a spike frequency of 122[Formula: see text]Hz. The power consumption and spike frequency rate depend on the input current. The proposed neuron is also capable of displaying both Type I and Type II frequency response characteristics. In addition, our neuron model underwent Monte Carlo simulation with 1000 sample runs to assess its robustness and stability against process variations and uncertainties. The results indicate that the average energy consumption of the proposed neuron model falls within the range of 16[Formula: see text]pJ to 68[Formula: see text]pJ.

Development of Bio-Voltage Operated Humidity-Sensory Neurons Comprising Self-Assembled Peptide Memristors.

Biomimetic humidity sensors offer a low-power approach for respiratory monitoring in early lung-disease diagnosis. However, balancing miniaturization and energy efficiency remains challenging. This study addresses this issue by introducing a bioinspired humidity-sensing neuron comprising a self-assembled peptide nanowire (NW) memristor with unique proton-coupled ion transport. The proposed neuron shows a low Ag+ activation energy owing to the NW and redox activity of the tyrosine (Tyr)-rich peptide in the system, facilitating ultralow electric-field-driven threshold switching and a high energy efficiency. Additionally, Ag+ migration in the system can be controlled by a proton source owing to the hydrophilic nature of the phenolic hydroxyl group in Tyr, enabling the humidity-based control of the conductance state of the memristor. Furthermore, a memristor-based neuromorphic perception neuron that can encode humidity signals into spikes is proposed. The spiking characteristics of this neuron can be modulated to emulate the strength-modulated spike-frequency characteristics of biological neurons. A three-layer spiking neural network with input neurons comprising these highly tunable humidity perception neurons shows an accuracy of 92.68% in lung-disease diagnosis. This study paves the way for developing bioinspired self-assembly strategies to construct neuromorphic perception systems, bridging the gap between artificial and biological sensing and processing paradigms.

Morpho-physiological Characteristics under Salinity Stress of Triticum aestivum L. var. Alex

The aim of the study was to highlight morpho-physiological changes like plant height, spike characteristics and stomata features in wheat induced by salt stress. Seeds of wheat var. Alex were sown, in mezo-phyto-cosmos, in five repetitions, under six saline doses, 0, 15, 30, 45, 60, and 75 mM NaCl. The experiment was conducted under field conditions. The assessments including plant height, arista, and spike length were taken and evaluated at the end of the experiment. Adaxial stomatal imprints have been sampled and the stomata density (number of stomata/mm2) and their size were assessed under the microscope. Wheat morphological parameters were affected by the highest salinity dose. Plant height was significantly reduced in the 60 and 75 mM NaCl doses by 16% and 20%. The spike length showed a significant reduction only in the highest saline dose treatment. Arista length registered the highest value in the 15 mM NaCl treatment with 23% more compared to the control. Stomata density reported the highest value in the 15 mM NaCl treatment, significantly higher compared to the 0 mM NaCl dose. The highest salinity doses significantly reduced plant height and spike length. Arista length and stomata density showed similar patterns.

The Cross-Correlation Between Spikes and Local Field Potentials at Different Running Speeds During Entorhinal Cortex Inactivation

Understanding neural spike dynamics and their interaction with local field potentials is crucial for deciphering neural connectivity. This study utilizes data generously shared by Zutshi et al. in 2022 (PMID: 34890566) to investigate spike characteristics and their cross-correlation with local field potentials. Our findings reveal significant shifts in the interspike interval (ISI) distributions during the medial entorhinal cortex (mEC) inactivation, with the instantaneous burst frequency exhibiting an overall decrease and preference for lower frequencies. These differences in the spike distributions were observed in individual cells, accompanied by changes in mean firing rates. Further exploration of the cross-correlation between spike multiunit activity (MUA) and the theta rhythm uncovers distinct coupling patterns. In the control cases, spikes closely align with theta peaks, whereas this alignment is disrupted during the mEC inactivation. Notably, we find that the strength of this cross-correlation is amplified at higher running speeds. This research enhances our understanding of neural dynamics, shedding light on the intricate relationship between spikes, local field potentials, and running speed, with potential implications for neural connectivity studies. R01MH126236 1R01MH109548-01A1. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

基于轻量级深度学习网络RepYOLO模型的麦穗实时检测

Real-time detection has become an essential component in intelligent agriculture and industry. In this paper, a real-time wheat spike detection method based on the lightweight deep learning network RepYOLO is proposed. Addressing the small and densely packed phenotype characteristics of wheat spikes, the channel attention mechanism module CBAM from the traditional YOLOv4 algorithm is introduced and multiple convolutional kernels are merged using a structural reparameterization method. Additionally, the ATSS algorithm is incorporated to enhance the accuracy of object detection. These approaches significantly reduce the model size, improve the inference speed, and lower the memory access cost. To validate the effectiveness of the model, it is trained and tested on a large dataset of diverse wheat spike images representing various phenotypes. The experimental results demonstrate that the RepYOLO algorithm achieves an average accuracy of 98.42% with a detection speed of 8.2 FPS. On the Jetson Nano platform, the inference speed reaches 34.20 ms. Consequently, the proposed model effectively reduces the memory access cost of deep learning networks without compromising accuracy and successfully improves the utilization of CPU/MCU limited performance.

Altered neurological and neurobehavioral phenotypes in a mouse model of the recurrent KCNB1-p.R306C voltage-sensor variant

Pathogenic variants in KCNB1 are associated with a neurodevelopmental disorder spectrum that includes global developmental delays, cognitive impairment, abnormal electroencephalogram (EEG) patterns, and epilepsy with variable age of onset and severity. Additionally, there are prominent behavioral disturbances, including hyperactivity, aggression, and features of autism spectrum disorder. The most frequently identified recurrent variant is KCNB1-p.R306C, a missense variant located within the S4 voltage-sensing transmembrane domain. Individuals with the R306C variant exhibit mild to severe developmental delays, behavioral disorders, and a diverse spectrum of seizures. Previous in vitro characterization of R306C described altered sensitivity and cooperativity of the voltage sensor and impaired capacity for repetitive firing of neurons. Existing Kcnb1 mouse models include dominant negative missense variants, as well as knockout and frameshifts alleles. While all models recapitulate key features of KCNB1 encephalopathy, mice with dominant negative alleles were more severely affected. In contrast to existing loss-of-function and dominant-negative variants, KCNB1-p.R306C does not affect channel expression, but rather affects voltage-sensing. Thus, modeling R306C in mice provides a novel opportunity to explore impacts of a voltage-sensing mutation in Kcnb1. Using CRISPR/Cas9 genome editing, we generated the Kcnb1R306C mouse model and characterized the molecular and phenotypic effects. Consistent with the in vitro studies, neurons from Kcnb1R306C mice showed altered excitability. Heterozygous and homozygous R306C mice exhibited hyperactivity, altered susceptibility to chemoconvulsant-induced seizures, and frequent, long runs of slow spike wave discharges on EEG, reminiscent of the slow spike and wave activity characteristic of Lennox Gastaut syndrome. This novel model of channel dysfunction in Kcnb1 provides an additional, valuable tool to study KCNB1 encephalopathies. Furthermore, this allelic series of Kcnb1 mouse models will provide a unique platform to evaluate targeted therapies.

The effect of multiscale parameters on the spiking properties of the morphological neuron with excitatory autapse

The excitatory autapse has been discovered recently by researchers in the pyramidal neurons, which could regulate the neuronal firing, while the spiking properties of the neuron with excitatory autapse (NWEA) have not been adequately studied. To further shorten such a gap, in this paper, a mathematical model is constructed for the NWEA to examine its spiking characteristics from two large scales: the external factors (which include the magnitude and location of the electrode current and the ambient temperature) and the internal factors (which consist of the location and maximum conductance of the excitatory autapse). The interspike interval (ISI), the spiking count (SC) and the spiking rhythm (SR) are used to analyse the spike trains. It is shown that the numerical results are coincident with the biological experiments, i.e. the excitatory autapse wholly enhances the bursting phenomenon and increases the neuronal responsiveness. In addition, as the maximum conductance of the excitatory autapse increases, the SC is gradually reduced but the SR is more active. With the variation of the current locations, the spike trains of the NWEA are more sensitive than those of the neuron without excitatory autapse. Furthermore, the ISI, SC and SR are all sensitive to the five types of parameters in various degrees. The results obtained in this paper may provide some instructions in regulating the firing of NWEA from the above five factors, and the proposed model for the NWEA provides a basis to perform more complex studies for the neurons with autapse.

Effects of spring low-temperature stress on winter wheat seed-setting characteristics of spike

Effects of spring low-temperature stress on winter wheat seed-setting characteristics of spike

Numerical investigation of nonlinear aeroelastic characteristics of a supersonic drag-reduction spike

The drag-reduction spike is widely employed in the testing of hypersonic vehicles owing to its remarkable ability to reduce drag and heat. Further investigation into the aeroelastic characteristics and mechanisms of the drag-reduction spike will offer a valuable reference for the refined design of its aerodynamics and structure. In this paper, based on the in-house high-order accurate CFD/CSD coupling analysis method, the variation law and mechanism of the nonlinear aeroelastic characteristics of the drag-reduction spike under different Mach numbers and dynamic pressures are studied. The results indicate that the nonlinear dynamic response of the drag-reduction spike transitions from limit cycle oscillations to more complex oscillatory characteristics as the Mach number increases from 2.0 to 5.0. Simultaneously, it is observed that the amplitude of the structural response also increases correspondingly. The primary factor contributing to this phenomenon is the elevated Mach number, which intensifies the interaction of unsteady aerodynamic loads on the surface of the spike. As a result, the location of the separation points and the structure of the separation vortex on the upper and lower surfaces become significantly asymmetric. Furthermore, as the dynamic pressure increases, the nonlinear dynamic response transitions from a complex characteristic to a stable limit cycle oscillation, accompanied by a corresponding increase in the structural amplitude. In both cases, the dynamical characteristics are mainly dominated by the first-order bending mode, and the vibration frequency remains consistent over time. Besides, the variation law of the flow field with the oscillation of the drag-reduction spike is summarized. As the spike is displaced, the complexity of the separation vortex and the intensity of the reattachment shock wave on the offset side gradually decrease, while the reverse is true on the other side. Meanwhile, a vortex is generated on the blunt body on the offset side of the spike, and the vortex region becomes larger as the offset increases.

Image-based classification of wheat spikes by glume pubescence using convolutional neural networks.

Pubescence is an important phenotypic trait observed in both vegetative and generative plant organs. Pubescent plants demonstrate increased resistance to various environmental stresses such as drought, low temperatures, and pests. It serves as a significant morphological marker and aids in selecting stress-resistant cultivars, particularly in wheat. In wheat, pubescence is visible on leaves, leaf sheath, glumes and nodes. Regarding glumes, the presence of pubescence plays a pivotal role in its classification. It supplements other spike characteristics, aiding in distinguishing between different varieties within the wheat species. The determination of pubescence typically involves visual analysis by an expert. However, methods without the use of binocular loupe tend to be subjective, while employing additional equipment is labor-intensive. This paper proposes an integrated approach to determine glume pubescence presence in spike images captured under laboratory conditions using a digital camera and convolutional neural networks. Initially, image segmentation is conducted to extract the contour of the spike body, followed by cropping of the spike images to an equal size. These images are then classified based on glume pubescence (pubescent/glabrous) using various convolutional neural network architectures (Resnet-18, EfficientNet-B0, and EfficientNet-B1). The networks were trained and tested on a dataset comprising 9,719 spike images. For segmentation, the U-Net model with EfficientNet-B1 encoder was chosen, achieving the segmentation accuracy IoU = 0.947 for the spike body and 0.777 for awns. The classification model for glume pubescence with the highest performance utilized the EfficientNet-B1 architecture. On the test sample, the model exhibited prediction accuracy parameters of F1 = 0.85 and AUC = 0.96, while on the holdout sample it showed F1 = 0.84 and AUC = 0.89. Additionally, the study investigated the relationship between image scale, artificial distortions, and model prediction performance, revealing that higher magnification and smaller distortions yielded a more accurate prediction of glume pubescence.

Neural heterogeneity controls computations in spiking neural networks.

The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics. How does this neural heterogeneity influence macroscopic neural dynamics, and how might it contribute to neural computation? In this work, we use a mean-field model to investigate computation in heterogeneous neural networks, by studying how the heterogeneity of cell spiking thresholds affects three key computational functions of a neural population: the gating, encoding, and decoding of neural signals. Our results suggest that heterogeneity serves different computational functions in different cell types. In inhibitory interneurons, varying the degree of spike threshold heterogeneity allows them to gate the propagation of neural signals in a reciprocally coupled excitatory population. Whereas homogeneous interneurons impose synchronized dynamics that narrow the dynamic repertoire of the excitatory neurons, heterogeneous interneurons act as an inhibitory offset while preserving excitatory neuron function. Spike threshold heterogeneity also controls the entrainment properties of neural networks to periodic input, thus affecting the temporal gating of synaptic inputs. Among excitatory neurons, heterogeneity increases the dimensionality of neural dynamics, improving the network's capacity to perform decoding tasks. Conversely, homogeneous networks suffer in their capacity for function generation, but excel at encoding signals via multistable dynamic regimes. Drawing from these findings, we propose intra-cell-type heterogeneity as a mechanism for sculpting the computational properties of local circuits of excitatory and inhibitory spiking neurons, permitting the same canonical microcircuit to be tuned for diverse computational tasks.

Spike rate dependent synaptic characteristics in lamellar, multilayered alpha-MoO3 based two-terminal devices - efficient way to control the synaptic amplification.

Brain-inspired computing systems require a rich variety of neuromorphic devices using multi-functional materials operating at room temperature. Artificial synapses which can be operated using optical and electrical stimuli are in high demand. In this regard, layered materials have attracted a lot of attention due to their tunable energy gap and exotic properties. In the current study, we report the growth of layered MoO3 using the chemical vapor deposition (CVD) technique. MoO3 has an energy gap of 3.22 eV and grows with a large aspect ratio, as seen through optical and scanning electron microscopy. We used transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy for complete characterisation. The two-terminal devices using platinum (Pt/MoO3/Pt) exhibit superior memory with the high-resistance state (HRS) and low-resistance state (LRS) differing by a large resistance (∼MΩ). The devices also show excellent synaptic characteristics. Both optical and electrical pulses can be utilised to stimulate the synapse. Consistent learning (potentiation) and forgetting (depression) curves are measured. Transition from long term depression to long term potentiation can be achieved using the spike frequency dependent pulsing scheme. We have found that the amplification of postsynaptic current can be tuned using such frequency dependent spikes. This will help us to design neuromorphic devices with the required synaptic amplification.

Valproate treatment induces age- and sex-dependent neuronal activity changes according to a patch clamp study.

Autism spectrum disorder is a heterogeneous neurodevelopmental disorder characterized by impaired social interactions, restricted, and stereotyped behaviors. The valproic acid model is one of the most recognized and broadly used models in rats to induce core symptoms of this disorder. Comorbidity of epilepsy and autism occurs frequently, due to similar background mechanisms that include the imbalance of excitation and inhibition. In this series of experiments, treatment was performed on rat dams with a single 500mg/kg dose i.p. valproate injection on embryonic day 12.5. Intracellular whole-cell patch clamp recordings were performed on brain slices prepared from adolescent and adult offspring of both sexes on pyramidal neurons of the medial prefrontal cortex and entorhinal cortex. Current clamp stimulation utilizing conventional current step protocols and dynamic clamp stimulation were applied to assess neuronal excitability. Membrane properties and spiking characteristics of layer II-III pyramidal cells were analyzed in both cortical regions. Significant sex-dependent and age-dependent differences were found in several parameters in the control groups. Considering membrane resistance, rheobase, voltage sag slope, and afterdepolarization slope, we observed notable changes mainly in the female groups. Valproate treatment seemed to enhance these differences and increase network excitability. However, it is possible that compensatory mechanisms took place during the maturation of the network while reaching the age-group of 3 months. Based on the results, the expression of the hyperpolarization-activated cyclic nucleotide-gated channels may be appreciably affected by the valproate treatment, which influences fundamental electrophysiological properties of the neurons such as the voltage sag. Remarkable changes appeared in the prefrontal cortex; however, also the entorhinal cortex shows similar tendencies.

Sea Clutter Suppression Using Smoothed Pseudo-Wigner–Ville Distribution–Singular Value Decomposition during Sea Spikes

The detection of small targets within the background of sea clutter is a significant challenge faced in radar signal processing. Small target echoes are weak in energy, and can be submerged by sea clutter and sea spikes, which are caused by overturning waves and breaking waves. This severely affects the radar target detection performance. This paper proposes a smoothed pseudo-Wigner–Ville distribution–singular value decomposition (SPWVD-SVD) method for sea clutter suppression. This method determines the instantaneous frequency range of the target by contrasting the time–frequency characteristics of the sea spike and the target. Subsequently, it employs a singular value difference spectrum to reduce the rank of the Hankel matrix, thereby reducing the computational burden of the instantaneous frequency estimation step in the experiment. Based on the instantaneous frequency range of the target in the time–frequency domain, the singular values of the target signal are retained, while the singular values of clutter are set to zero. This process accomplishes the reconstruction of radar echo signals and effectively achieves the suppression of sea clutter. The suppression effect is verified using simulation data alongside ten sets of Intelligent Pixel processing X-band (IPIX) radar data against the background of sea spikes. By contrasting the clutter amplitudes before and after suppression, the SPWVD-SVD algorithm demonstrated an average clutter suppression of 15.06 dB, which proves the effectiveness of the proposed algorithm in suppressing sea clutter.