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In the brain, endoplasmic reticulum (ER) stress results in synaptic dysfunction and eventually leads to neurodegeneration. Allomyrina dichotoma larvae are a Chinese ethnomedicine and are widely used in East Asia. In the present study, we investigated the ability of ethanol extract of A. dichotoma larvae (ADE) to improve synaptic structure and function by activating unfolded protein response (UPR) under ER stress in animal and neuron culture models. ER stress was induced in obese mice fed a high fat diet (HFD) or by treating dissociated cultures of rat embryonic (E19) hippocampal neurons with tunicamycin (TM). Western blot and real-time or conventional RT-PCR were performed to analyze the expressions of ER stress marker proteins. In dissociated hippocampal cultures, immunocytochemistry was performed for synaptic proteins, and cultures were stained with styryl dye FM1-43 to assess presynaptic activities. In HFD-fed obese mice, ADE efficiently reduced the expressions of ER stress markers, such as, xbp-1, chop, atf4, erdi4, and eIf2a, and those of the ER chaperone/foldases Bip/grp78, Ero-1l, and PDI. Unconventionally spliced xbp-1s mRNA was not detected. In primary rat hippocampal cultures under ER stress, ADE significantly lowered the nuclear expression of CHOP, inhibited the downregulations of postsynaptic proteins, such as, GluN2A, GluN2B, and PSD-95, and maintained the pool size of recycling presynaptic vesicles. The study shows that ADE potently suppressed the induction of ER stress and maintained the structure and function of hippocampal neurons, and suggests that ADE is a potentially valuable food supplement and preventive therapeutic for ER stress-related nervous disorders.

The bio-mimetic structure of a neuron is taken into account for utilizing the electrophysiological data. These neuron circuits are entertained for the use in digital computers. At the end of Moore’s law, conventional technology is striking different difficulties, such as power consumption, area utilization, and energy efficiency. To conquer these hurdles, a nanoscale, the nonvolatile memristor used in the proposed neuron modified from the refined AH neuron. Synapses are also built using anti-parallel memristors. These neurons and synapse are joined together such that the performance metrics are analyzed the energy consumption is reduced by 89.656%. Besides, power consumption is limited by 37.568% and the spike frequency is measured as 10.263kHz when compared with the traditional CMOS synapse connected with the proposed neuron. Moreover, the measured energy per spike is 3.37fJ. The implementation of the neuron network is done by 45nm technology.

The neuristor based on memristors can be used to mimic synapse and neurons of biological neural systems, and it is the key unit of spiking neural networks. However, the resistance states of realistic memristors are nonvolatile, which is not conducive to mimicking the forgetting function of the brain. Given that the resistance states of memristor emulators are volatile after power down, this paper exhibits a scalable neuristor built with a half-wave memristor emulator. The proposed neuristor demonstrates four critical features for action-potential-based computing: the all-or-nothing spiking of an action potential, threshold-driven spiking, diverse periodic spiking and symmetric anti-Hebbian learning rule of spike-timing-dependent plasticity. Particularly, there are no complex shape and duration constraints on pre- and post-spikes for implementing the symmetric anti-Hebbian learning rule.

In-phase burst synchronization, spatiotemporal order and rhythm dynamics of a complex neuronal network with electrical or chemically excitatory synapses are studied in this paper. A quantitative characteristic, the width factor, is introduced to describe the rhythm dynamics of an individual neuron, and the average width factor is used to characterize the rhythm dynamics of a neural network. The in-phase burst synchronization is studied in terms of the burst phase order parameter. We also study the effects of the coupling schemes, the intrinsic neuronal property and the network topology on the rhythm dynamics of the network. It is found that the neuronal network with electrical coupling is easier to realize the in-phase burst synchronization than that with the chemically excitatory coupling. The bursting type of short bursting neuronal networks is unchanged for different coupling schemes with the coupling strength increasing. Moreover, the short bursting type is robust both to the coupling strength and the coupling scheme. As for the network topology, more links can only change the bursting type of long bursting neurons, but short bursting neurons are robust to the link numbers.

The work investigates the influence of spike-timing dependent plasticity (STDP) mechanisms on the dynamics of two synaptically coupled neurons driven by additive external noise. In this setting, the noise signal models synaptic inputs that the pair receives from other neurons in a larger network. We show that in the absence of STDP feedbacks the pair of neurons exhibit oscillations and intermittent synchronization. When the synapse connecting the neurons is supplied with a phase selective feedback mechanism simulating STDP, induced dynamics of spikes in the coupled system resembles a phase locked mode with time lags between spikes oscillating about a specific value. This value, as we show by extensive numerical simulations, can be set arbitrary within a broad interval by tuning parameters of the STDP feedback.

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The design and mathematical modeling of the programing electric field in a neural switch is carried out. The specified function for the switch is to operate as a synaptic processor behaving in an adaptive manner and suitable to be used as a compact programable device with other artificial neural network hardware. Modeling of the switch is carried out by means of complex mathematical analysis employing the Schwarz–Christoffel transform. The effect of inter-electrode separation on the field strength is analyzed in two dimensions. The realized power law function of the programing field is discussed and explained.

A recently developed quantitative model of cortical activity is used that permits data comparison with experiment using a quantitative and standardized means. The model incorporates properties of neurophysiology including axonal transmission delays, synapto-dendritic rates, range-dependent connectivities, excitatory and inhibitory neural populations, and intrathalamic, intracortical, corticocortical and corticothalamic pathways. This study tests the ability of the model to determine unique physiological properties in a number of different data sets varying in mean age and pathology. The model is used to fit individual electroencephalographic (EEG) spectra from post-traumatic stress disorder (PTSD), schizophrenia, first episode schizophrenia (FESz), attention deficit hyperactivity disorder (ADHD), and their age/sex matched controls. The results demonstrate that the model is able to distinguish each group in terms of a unique cluster of abnormal parameter deviations. The abnormal physiology inferred from these parameters is also consistent with known theoretical and experimental findings from each disorder. The model is also found to be sensitive to the effects of medication in the schizophrenia and FESz group, further supporting the validity of the model.

It is well established that membrane receptors, transporters, and ion channels are organized into functional networks at the cell membrane by multiprotein complexes. The scaffolding proteins physically link these signaling membrane proteins to their intracellular effectors and actin skeleton. The last ten years of research in the field have revealed the nature, structure, and functions of some of these multiprotein complexes. Here, we will focus on those which are present at the excitatory glutamatergic synapse and describe some of their structural and functional aspects, as well as the main methods which are use to study them.

It is well known that neurons communicate through synapses in the nervous system, and the size, morphology, and connectivity of synapses determine the functional properties of the neural network. Therefore, synapses have always been one of the key objects of neuroscience. Due to the technical advance in electron microscope (EM), the physical structure of synapses can be observed at high resolution. Nevertheless, to date, the automatic analysis of the synapse in EM images is still a challenging task. In this paper, we proposed a fractal dimension-based segmentation method for synaptic clef of mouse cortex on EM image stack. Our method does not require a lot of groundtruth to train the model, and shows better adaptive anti-noise performance. That should be ascribed to the stability of segmentation-related key parameters in the data from same tissue. In this way, we only need to give initial values, and then gradually adjust these key parameters. Experiments reveal that our method achieves the desired results, and reduces the time in artificial annotating, so that researchers can focus more on the analysis of segmentation results.