با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Implementation of homeostasis functionality in neuron circuit using doublegate device for spiking neural network
اجرای عملکرد هموستاز در مدار نورون با استفاده از دستگاه دروازه دو تایی برای شبکه عصبی spiking -2020
The homeostatic neuron circuit using a double-gate MOSFET is proposed to imitate a homeostasis functionality of a biological neuron in spiking neural networks (SNN) based on a spike-timing dependent plasticity (STDP). The threshold voltage (Vth) of the double-gate MOSFET is controlled by independent two-gate biases (VG1 and VG2). By using Vth change of the double-gate MOSFET in the neuron circuits, the fire rate of the output neuron is controlled. The homeostasis functionality is implemented by the operation of multi-neuron system based on the proposed neuron circuit. Through the SNN based on STDP using MNIST datasets, it is demonstrated that the recognition rate (~91%) of the SNN with the proposed homeostasis functionality is higher than that (~79%) of the SNN without the proposed homeostasis functionality. Also, the results of the recognition rate with the variations (σ/μ < 0.5) of the synaptic devices and the initial Vth of neuron circuits show a low degradation (1 ~ 3%) in the recognition rate. Thus, it is demonstrated that the homeostasis functionality of the proposed neuron circuit has the immunity to variations (σ/μ < 0.5) of the synaptic devices and the neuron circuits in the SNN based on STDP.
Keywords: Double-gate MOSFET | Neuron circuit | Homeostasis functionality | Pattern recognition | Spiking neural networks (SNNs)
Towards the interpretation of complex visual hallucinations in termsof self-reorganization of neural networks
به سمت تفسیر توهمات پیچیده بصری از نظر خود سازماندهی مجدد شبکه های عصبی-2020
Patients suffering from dementia with Lewy body (DLB) often see complex visual hallucinations (CVH).Despite many pathological, clinical, and neuroimaging studies, the mechanism of CVH remains unknown.One possible scenario is that top-down information is being used to compensate for the lack of bottom-up information. To investigate this possibility and understand the underlying mathematical structureof the CVH mechanism, we propose a simple computational model of synaptic plasticity with particu-lar focus on the effect of selective damage to the bottom-up network on self-reorganization. We showneurons that undergo a change in activity from a bottom-up to a top-down network framework duringthe reorganization process, which can be understood in terms of state transitions. Assuming that thepre-reorganization representation of this neuron remains after reorganization, it is possible to interpretneural response induced by top-down information as the sensation of bottom-up information. This sit-uation might correspond to a hallucinatory situation in DLB patients. Our results agree with existingexperimental evidence and provide new insights into data that have hitherto not been experimentallyvalidated on patients with DLB.
Keywords : Network self-reorganization | Complex visual hallucinations| Synaptic plasticity | State transition | Oscillology
Serotonin induces Arcadlin in hippocampal neurons
سروتونین آرکادلین را در سلول های عصبی هیپوکامپ القا می کند-2020
The monoamine hypothesis does not fully explain the delayed onset of recovery after antidepressant treatment or the mechanisms of recovery after electroconvulsive therapy (ECT). The common mechanism that operates both in ECT and monoaminergic treatment presumably involves molecules induced in both of these conditions. A spine density modulator, Arcadlin (Acad), the rat orthologue of human Protocadherin-8 (PCDH8) and of Xenopus and zebrafish Paraxial protocadherin (PAPC), is induced by both electroconvulsive seizure (ECS) and antidepressants; however, its cellular mechanism remains elusive. Here we confirm induction of Arcadlin upon stimulation of an N-methyl-D-aspartate (NMDA) receptor in cultured hippocampal neurons. Stimulation of an NMDA receptor also induced acute (20 min) and delayed (2 h) phosphorylation of the p38 mitogen-activated protein (MAP) kinase; the delayed phosphorylation was not obvious in Acad–/– neurons, suggesting that it depends on Arcadlin induction. Exposure of highly mature cultured hippocampal neurons to 1–10 μM serotonin for 4 h resulted in Arcadlin induction and p38 MAP kinase phosphorylation. Co-application of the NMDA receptor antagonist D-(-)-2-amino-5-phosphonopentanoic acid (APV) completely blocked Arcadlin induction and p38 MAP kinase phosphorylation. Finally, administration of antidepressant fluoxetine in mice for 16 days induced Arcadlin expression in the hippocampus. Our data indicate that the Arcadlin-p38 MAP kinase pathway is a candidate neural network modulator that is activated in hippocampal neurons under the dual regulation of serotonin and glutamate and, hence, may play a role in antidepressant therapies.
Keywords: Serotonin | Protocadherin | Adhesion | Synaptic plasticity | Antidepressant | Mitogen-activated protein kinase
Information capacity of a network of spiking neurons
ظرفیت اطلاعاتی شبکه ای از نورون های spiking -2020
We study a model of spiking neurons, with recurrent connections that result from learning a set of spatio-temporal patterns with a spike-timing dependent plasticity rule and a global inhibition. We investigate the ability of the network to store and selectively replay multiple patterns of spikes, with a combination of spatial population and phaseof- spike code. Each neuron in a pattern is characterized by a binary variable determining if the neuron is active in the pattern, and a phase-lag variable representing the spiketiming order among the active units. After the learning stage, we study the dynamics of the network induced by a brief cue stimulation, and verify that the network is able to selectively replay the pattern correctly and persistently. We calculate the information capacity of the network, defined as the maximum number of patterns that can be encoded in the network times the number of bits carried by each pattern, normalized by the number of synapses, and find that it can reach a value αmax ≃ 0.27, similar to the one of sequence processing neural networks, and almost double of the capacity of the static Hopfield model. We study the dependence of the capacity on the global inhibition, connection strength (or neuron threshold) and fraction of neurons participating to the patterns. The results show that a dual population and temporal coding can be optimal for the capacity of an associative memory.
Keywords: Associative memory | Plasticity | Learning | Storage capacity | Neural networks | Spatiotemporal pattern replay
Shenqi Yizhi granules protect hippocampus of AD transgenic mice by modulating on multiple pathogenesis processes
Shenqi Yizhi granules protect hippocampus of AD transgenic mice by modulating on multiple pathogenesis processes-2020
Ethnopharmacological relevance: 3 Chinese herbal medicine (CHM) draws more attention to explore effective therapeutic 4 strategy for AD continuum. CHM usually uses combinations of herbs or herbal 5 ingredients to treat diseases, with the components targeting different disease processes. 6 CHM might improve cognition in AD and MCI patients by optimizing network 7 activity, promoting neural plasticity and repairing damaged neurons. Shenqi Yizhi 8 granules (SQYG), a Chinese herbal recipe, are mainly consists of Panax ginseng 9 C.A.Mey, Astragalus membranaceus (Fisch.) Bunge, and Scutellaria baicalensis 10 Georgi and have been used to ameliorate cognitive deficits in mild-to-moderate 11 dementia patients. 12 Aim of the study: To investigate the neuroprotection effect and pharmacotherapy 13 mechanism of SQYG in hippocampus of 5XFAD transgenic mice. 14 Materials and methods: The immunofluorescence detection, 2DE-gels, mass spectrum 15 identification, biological information analysis and western blot were performed after 16 SQYG treatment. 17 Results: SQYG treatment significantly decreased the fluorescence intensities of 18 anti-GFAP and anti-Iba1 in the hippocampus of 5XFAD mice. The expression levels of 31 proteins in the hippocampus were significantly influenced by SQYG, 20 approximately 65% of these proteins are related to energy metabolism, stress response 21 and cytoskeleton, whereas others are related to synaptic transmission, signal 22 transduction, antioxidation, amino acid metabolism, and DNA repair. The expression 23 of these proteins were increased. The changes in the expression of malate 24 dehydrogenase (cytoplasmic) and pyruvate kinase M were confirmed by western blot. 25 Conclusions: The pharmacotherapy mechanism of SQYG on the hippocampus may be 26 related to modulation of a number of physiological processes, including energy 27 metabolism, stress response, cytoskeleton, synaptic transmission, signal transduction, 28 and amino acid metabolism in 5XFAD mice.
Keywords: 31 Alzheimer’s disease | hippocampus | proteomics | Shenqi Yizhi granules | 5XFAD mice | 32 energy metabolism
Nutritional control of puberty in the bovine female: prenatal and early postnatal regulation of the neuroendocrine system
کنترل تغذیه ای بلوغ در گاو های ماده : تنظیم پیش از تولد و زودرس پس از زایمان سیستم عصبی و غدد درونریز-2020
Puberty is a complex biological event that requires maturation of the reproductive neuroendocrine axis and subsequent initiation of high-frequency, episodic release of GnRH and LH. Nutrition is a critical factor affecting the neuroendocrine control of puberty. Although nutrient restriction during juvenile development delays puberty, elevated rates of body weight gain during this period facilitate pubertal maturation by programming hypothalamic centers that underlie the pubertal process. Recent findings suggest that maternal nutrition during gestation can also modulate the development of the fetal neuroendocrine axis, thus influencing puberty and subsequent reproductive function. Among the several metabolic signals, leptin plays a critical role in conveying metabolic information to the brain and, consequently, controlling puberty. The effects of leptin on GnRH secretion are mediated via an upstream neuronal network because GnRH neurons do not express the leptin receptor. Two neuronal populations located in the arcuate nucleus that express the orexigenic peptide neuropeptide Y (NPY), and the anorexigenic peptide alpha melanocyte-stimulating hormone (aMSH), are key components of the neurocircuitry that conveys inhibitory (NPY) and excitatory (aMSH) inputs to GnRH neurons. In addition, neurons in the arcuate nucleus that coexpress kisspeptin, neurokinin B, and dynorphin (termed KNDy neurons) are also involved in the metabolic control of puberty. Our studies in the bovine female demonstrate that increased planes of nutrition during juvenile development lead to organizational and functional changes in hypothalamic pathways comprising NPY, proopiomelanocortin (POMC, the precursor of aMSH), and kisspeptin neurons. Changes include alterations in the abundance of NPY, POMC, and Kiss1 mRNA and in plasticity of the neuronal projections to GnRH neurons. Our studies also indicate that epigenetic mechanisms, such as modifications in the DNA methylation pattern, are involved in this process. Finally, our most recent data demonstrate that maternal nutrition during gestation can also induce morphological and functional changes in the hypothalamic NPY system in the heifer offspring that are likely to persist long after birth. These organizational changes occurring during fetal development have the potential to not only impact puberty but also influence reproductive performance throughout adulthood in the bovine female.
Keywords: Heifers | Hypothalamus | Leptin | Nutrition | Puberty
Influence of plastic properties on the grain size effect on twinning in Ti and Mg
تأثیر خواص پلاستیکی بر تأثیر اندازه دانه در دوقلوهای حاوی Ti و Mg-2020
Deformation twinning is an inevitable mode of plastic deformation in hexagonal close packed (HCP) crystals and has been considered a hinderance to achieving formability and ductility. It has long been recognized that reductions in grain size can lower the propensity for twinning. In prior work, EBSD statistical analyses on big data sets of twins in pure Ti and Mg were carried out for the same twin type, loading conditions, initial texture, and range of grain sizes. Here, we revisit these data sets to investigate the role of material plastic properties on grain size effects on deformation twinning. We show that while reductions in grain size dramatically lower the number of twins per grain and the twin thickness in both metals, these effects are several times stronger in Ti than that for Mg. To rationalize this difference, full-field, 3D crystal plasticity calculations are performed. The analysis indicates that the stronger grain size effect in Ti arises due to the larger critical resolved shear stress (CRSS) of its easiest slip system, making Ti less accommodating of the twin shear than Mg. This result points to plastic properties as additional variables that can be tuned towards developing HCP materials for structural applications.
Keywords: Polycrystal | Magnesium | Titanium | Plasticity | Grain size
Using neural networks to represent von Mises plasticity with isotropic hardening
استفاده از شبکه های عصبی برای نشان دادن انعطاف پذیری von Mises با سخت شدن ایزوتروپیک-2020
Neural networks are universal function approximators that form the backbone of most modern machine learning based models. Starting from a conventional return-mapping scheme, the algorithmic description of von Mises plasticity with isotropic hardening is mathematically reformulated such that the relationship between the strain and stress histories may be modeled through a neural network function. In essence, the neural network provides an estimate of the instantaneous elasto-plastic tangent matrix as a function of the current stress and plastic work density. For plane stress conditions, it thus describes a non-linear mapping from ℝ4 to ℝ6. The neural network function is first developed for uniaxial stress conditions including loading histories with tension-compression reversal. Special attention is paid to the identification of the network architecture and artifacts related to overfitting. Furthermore, the performance of networks featuring the same number of total parameters, but different levels of non-linearity, is compared. It is found that a fully-connected feedforward network with five hidden layers and 15 neurons per layer can describe the plane stress plasticity problem with good accuracy. The results also show that a high density of training data (of the order of ten to hundred thousand points) is needed to obtain reasonable estimates for arbitrary loading paths with strains of up to 0.2. The final neural network model is implemented into finite element software through the user material subroutine interface. A simulation of a notched tension experiment is performed to demonstrate that the neural network model yields the same (heterogeneous) mechanical fields as a conventional J2 plasticity model. The present work demonstrates that it is feasible to describe the stressstrain response of a von Mises material through a neural network model without any explicit representation of the yield function, flow rule, hardening law or evolution constraints. It is emphasized that the demonstration of feasibility is the focus of the present work, while the assessment of potential computational advantages is deferred to future research.
Keywords: J2 plasticity | Machine learning | Elasto-plastic tangent modulus | Return mapping
A review of learning in biologically plausible spiking neural networks
مروری بر یادگیری در شبکه های عصبی اسپایک بیولوژیکی قابل قبول-2020
Artificial neural networks have been used as a powerful processing tool in various areas such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has encouraged researchers to improve artificial neural networks by investigating the biological brain. Neurological research has significantly progressed in recent years and continues to reveal new characteristics of biological neurons. New technologies can now capture temporal changes in the internal activity of the brain in more detail and help clarify the relationship between brain activity and the perception of a given stimulus. This new knowledge has led to a new type of artificial neural network, the Spiking Neural Network (SNN), that draws more faithfully on biological properties to provide higher processing abilities. A review of recent developments in learning of spiking neurons is presented in this paper. First the biological background of SNN learning algorithms is reviewed. The important elements of a learning algorithm such as the neuron model, synaptic plasticity, information encoding and SNN topologies are then presented. Then, a critical review of the state-of-the-art learning algorithms for SNNs using single and multiple spikes is presented. Additionally, deep spiking neural networks are reviewed, and challenges and opportunities in the SNN field are discussed.
Keywords: Spiking neural network (SNN) | Learning | Synaptic plasticity
Formation, structure and properties of pseudo-high entropy clustered bulk metallic glasses
تشکیل ، ساختار و خصوصیات شیشه های فلزی انبوه خوشه ای آنتروپی شبه زیاد-2020
The formation, structure and properties of clustered glassy alloys were reviewed in correlation with pseudo-high entropy (PHE) type alloy components on the basis of our recent data. The features of the PHE bulk metallic glasses (BMGs) in Fe- and Zr-based alloy systems were also presented in comparison with high entropy glassy and crystalline alloys. The selection of PHE alloy compositions enables the formation of a clustered glassy phase in the wide temperature range below the onset temperature of the second exotherm peak. The presence of the glassy phase without appreciable precipitates even after the completion of the first exotherm peak has not been reported so far in spite of a large number of crystallization data on amorphous and glassy alloys. The precipitates at the temperatures just below the second exotherm peak for PHE Fe- and Zr-based glassy alloys are big cubic c-FeCoNiCrMo(B) with a lattice parameter of about 0.90 nm and Zr2M (M ¼ Al,Fe,Co,Ni,Ag) with a lattice parameter of about 1.2 nm, respectively. The precipitation of these metastable phases with large unit volumes needs longrange atomic rearrangements, but the atomic diffusivity in the PHE glassy alloys is very low, resulting in the formation of the clustered glassy phase. The clustered glassy alloys exhibit high strength and unprecedented plasticity for PHE ZreNbeAleNieCu BMGs, very high coercive force for Nd-Fe-Al BMGs, good soft magnetic properties in conjunction with better plasticity for PHE FeeBeSiePeCu BMG and high thermal resistance to crystallization for PHE ZreAleNieCueAg BMGs, and hence are expected to become important as a new type of engineering glassy material
Keywords: Bulk metallic glasses | Pseudo-high entropy alloys | Clustered glassy phase