با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Intranasal oxytocin enhances EEG mu rhythm desynchronization during execution and observation of social action: An exploratory study
اکسی توسین داخل رحمی باعث می شود که EEG mu رطوبت زدایی در حین اجرا و مشاهده اقدامات اجتماعی تقویت شود: یک مطالعه اکتشافی-2020 Intranasal administration of oxytocin (OT) has been found to facilitate prosocial behaviors, emotion recognition
and cooperation between individuals. Recent electroencephalography (EEG) investigations have reported enhanced
mu rhythm (alpha: 8–13 Hz; beta: 15–25 Hz) desynchronization during the observation of biological
motion and stimuli probing social synchrony after the administration of intranasal OT. This hormone may
therefore target a network of cortical circuits involved in higher cognitive functions, including the mirror neuron
system (MNS). Here, in a double-blind, placebo-controlled, between-subjects exploratory study, we investigated
whether intranasal OT modulates the cortical activity from sensorimotor areas during the observation and the
execution of social and non-social grasping actions. Participants underwent EEG testing after receiving a single
dose (24 IU) of either intranasal OT or placebo. Results revealed an enhancement of alpha - but not beta -
desynchronization during observation and execution of social grasps, especially over central and parietal electrodes,
in participants who received OT (OT group). No differences between the social and non-social condition
were found in the control group (CTRL group). Moreover, we found a significant difference over the cortical
central-parietal region between the OT and CTRL group only within the social condition. These results suggest a
possible action of intranasal OT on sensorimotor circuits involved in social perception and action understanding,
which might contribute to facilitate the prosocial effects typically reported by behavioral studies. Keywords: Oxytocin | ERD | Mirror neuron system | Grasping actions | Electroencephalogram |
مقاله انگلیسی |
12 |
Prognosis of multiple instances in time-aware declarative business process models
پیش بینی موارد متعدد در مدل های فرآیند کسب و کار آگاه از زمان-2020 Technological evolution, heading for industry 4.0, makes companies tend to automate their management
and operation, ideally defining it through business process models. To describe policies or rules related
to the execution order of the activities in an organization, Declarative Business Process Models permit
a relaxed description of activity order, which needs monitoring to detect non-conforming behaviors.
Commonly, the detection of a violation implies that the malfunction has already occurred, being better
to avoid the violation in advance. To predict future violations, prognosis is required.
To allow the modeling of real business behavior, an extension of declarative business process models
including both time patterns and multiple instances is proposed. This new model can be used to prognosticate if current process instances may violate a defined model in the future, according to the analysis
of the robustness of the process instances evolution. The proposed Model-Based Prognosis is based on
analyzing the event traces that represent the current instances and propagate their possible progression
through the Constraint Programming paradigm. To ascertain if the model could be violated, it is analyzed
how its robustness can tackle unexpected behaviors.
To complete the formalization and modeling, an implementation applied to a real medical example is
included in the paper. The prognosis of concurrent instances is addressed, dealing with formalized time
and activity patterns even considering the resource availability, and getting acceptable execution times.
The automatic verification and prognosis of declarative business processes are addressed considering
concurrency and synchronization of multiple instances, performing well in terms of execution time. Keywords: Declarative business processes | Multiple instances | Model-based prognosis | Robustness |
مقاله انگلیسی |
13 |
FPGA Realization of Fractional Order Neuron
تحقق FPGA نورون مرتبه فراکسیون-2020 In this paper fractional Hindmarsh Rose (HR) neuron, which mimics several behaviors of a real biological neuron is implemented on field programmable gate array (FPGA). The re- sults show several differences in the dynamic characteristics of integer and fractional order Hindmarsh Rose neuron models. The integer order model shows only one type of firing characteristics when the parameters of model remains same. The fractional order model depicts several dynamical behaviors even for the same parameters as the order of the fractional operator is varied. The firing frequency increases when the order of the frac- tional operator decreases. The fractional order is therefore key in determining the firing characteristics of biological neurons. To implement this neuron model first the digital re- alization of different fractional operator approximations are obtained, then the fractional integrator is used to obtain the low power and low cost hardware realization of fractional HR neuron. The fractional neuron model has been implemented on a low voltage and low power circuit and then compared with its integer counter part. The hardware is used to demonstrate the different dynamical behaviors of fractional HR neuron for different type of approximations obtained for fractional operator in this paper. A coupled network of frac- tional order HR neurons is also implemented. The results also show that synchronization between neurons increases as long as coupling factor keeps on increasing. Keywords: Computational neuroscience | Fractional Hindmarsh Rose neuron (HR) | Fractional calculus | Fractional-operator | Field-programmable-gate-arrays (FPGA) | Synchronization |
مقاله انگلیسی |
14 |
Channel noise effects on neural synchronization
تأثیر نویز کانال بر هماهنگی عصبی-2020 Synchronization in neural networks is believed to be linked to cognitive processes,
while abnormal synchronization has been associated with disorders such as epilepsy
and schizophrenia. We examine the synchronization of small Hodgkin–Huxley neuronal
networks. The principal features of Hodgkin–Huxley neurons are protein channels in
the neural membrane that transition between open and closed states with voltage
dependent rate constants. The standard assumption of infinitely many channels neglects
the fact that real neurons have finitely many channels, which leads to fluctuations in the
membrane voltage and modifies neuronal spike times. These fluctuations are referred to
as channel noise. We demonstrate that regardless of channel noise magnitude, neurons
in the network reach a steady state synchronization level dependent only on the
number of neurons in the network, equivalent to the steady state level of uncoupled
Poisson neurons. The channel noise only affects the time to reach the steady state
synchronization level. Keywords: Synchronization | Hodgkin–Huxley | Channel noise | Neural network |
مقاله انگلیسی |
15 |
Chimera States mediated by nonlocally attractive-repulsive coupling in FitzHugh–Nagumo neural networks
حالت Chimera با واسطه زوج جذب-دفع در شبکه های عصبی FitzHugh-Nagumo-2020 The spontaneous occurrence of heterogeneous behaviors in homogeneous systems is an intriguing
phenomenon. Recently, a remarkable heterogeneous behavior, called “chimera states”, which
consists of spatially coherent and incoherent domains, has been studied in a great variety of
systems including physical, chemical, biological, or optical. In this paper, chimera states in
FitzHugh–Nagumo (FHN) neural networks are investigated. The identical FHN neurons are assigned
in a ring and nonlocally coupled by attractive and repulsive couplings. We show that, the
chimera states can be induced by the cooperation of nonlocally attractive and repulsive interactions
between these neurons. Moreover, depending on the strength and range of attractive or
repulsive couplings, the neural networks display different spatiotemporal behaviors, including
chimera states, multi-cluster (MC) chimera states, traveling waves, traveling coherent states,
solitary states, bursting synchronizations, and synchronizations. These results suggest that attractive
and repulsive couplings may play a crucial role in mediating dynamic behavior of neural
networks, and these results could be useful in understanding and predicting the rich dynamics of
neural networks. Keywords: Attractive and repulsive coupling | Neural network | Chimera state |
مقاله انگلیسی |
16 |
Synchronization of Hindmarsh Rose Neurons
هماهنگ سازی از نورون های Hindmarsh Rose-2020 Modeling and implementation of biological neurons are key to the fundamental understanding of
neural network architectures in the brain and its cognitive behavior. Synchronization of neuronal
models play a significant role in neural signal processing as it is very difficult to identify the actual
interaction between neurons in living brain. Therefore, the synchronization study of these neuronal
architectures has received extensive attention from researchers. Higher biological accuracy of these
neuronal units demands more computational overhead and requires more hardware resources for
implementation. This paper presents a two coupled hardware implementation of Hindmarsh Rose
neuron model which is mathematically simpler model and yet mimics several behaviors of a real
biological neuron. These neurons are synchronized using an exponential function. The coupled system
shows several behaviors depending upon the parameters of HR model and coupling function. An
approximation of coupling function is also provided to reduce the hardware cost. Both simulations and
a low cost hardware implementations of exponential synaptic coupling function and its approximation
are carried out for comparison. Hardware implementation on field programmable gate array (FPGA)
of approximated coupling function shows that the coupled network produces different dynamical
behaviors with acceptable error. Hardware implementation shows that the approximated coupling
function has significantly lower implementation cost. A spiking neural network based on HR neuron
is also shown as a practical application of this coupled HR neural networks. The spiking network
successfully encodes and decodes a time varying input. Keywords: Computational neuroscience | Hindmarsh Rose neuron (HR) | Digital | Spiking Neural Networks (SNNs) | Field programmable gate arrays (FPGAs) | Nengo |
مقاله انگلیسی |
17 |
Reinforcement learning based two-level control framework of UAV swarm for cooperative persistent surveillance in an unknown urban area
چارچوب کنترل دو سطح مبتنی بر یادگیری تقویتی برای پایش مداوم همکاری در یک منطقه ناشناخته شهری-2020 Persistent surveillance in a complex unknown urban area by an unmanned aerial vehicle (UAV) swarm is a low-cost, promising future application for anti-terrorism, disaster monitoring, and battlefield situational awareness. Based on over-simplified simulated surroundings and a UAV dynamic model, a few remarkable approaches have been proposed; however, they typically rely on non-sensor-based inputs and prior knowledge on the environment or targets. To overcome these limitations, based on simulated city blocks, a two-level quasi-distributed control framework is proposed for realizing the continuous control of a UAV swarm in two defined surveillance phases. With the support of a well-trained and corrected artificial neural network (ANN) in low-level UAV manoeuvre control for target homing and collision avoidance, several preliminary high-level target allocation strategies are designed for a cooperative overall objective based on the synchronization of local surveillance data. Then, via a series of numerical simulations, an optimal high-level strategy combination is identified. Finally, the surveillance performance of this strategy combination is evaluated under various swarm sizes and UAV launching patterns. The simulation results demonstrate that the proposed control framework is applicable for UAV swarm control in the persistent surveillance of unknown urban areas. Keywords: UAV swarm | Reinforcement learning | Persistent surveillance | Autonomous manoeuvre control | Artificial neural network (ANN) |
مقاله انگلیسی |
18 |
A Distinct Class of Bursting Neurons with Strong Gamma Synchronization and Stimulus Selectivity in Monkey V1
یک کلاس متمایز از پشت سر گذاشتن نورون ها با همگام سازی گاما قوی و انتخاب محرک در میمون V1-2020 Cortical computation depends on interactions between
excitatory and inhibitory neurons. The contributions
of distinct neuron types to sensory processing
and network synchronization in primate visual
cortex remain largely undetermined. We show that
in awake monkey V1, there exists a distinct cell
type (ii30% of neurons) that has narrow-waveform
(NW) action potentials and high spontaneous
discharge rates and fires in high-frequency bursts.
These neurons are more stimulus selective and
phase locked to 30- to 80-Hz gamma oscillations
than other neuron types. Unlike other neuron types,
their gamma-phase locking is highly predictive
of orientation tuning. We find evidence for strong
rhythmic inhibition in these neurons, suggesting
that they interact with interneurons to act as excitatory
pacemakers for the V1 gamma rhythm. We
did not find a similar class of NW bursting neurons
in L2-L4 of mouse V1. Given its properties, this class
of NW bursting neurons should be pivotal for the
encoding and transmission of stimulus information. |
مقاله انگلیسی |
19 |
Adaptive cache pre-forwarding policy for distributed deep learning
سیاست پیش هدایت حافظه پنهان تطبیقی برای یادگیری عمیق توزیع شده-2020 With the rapid growth of deep learning algorithms, several high-accuracy models have been developed and applied to many real-world domains. Deep learning is parallel and suitable for distributed computing, which can significantly improve the system through- put. However, there is a bottleneck for cross-machine training, that is, network latency. Nodes frequently need to wait for synchronization, and the content of each synchroniza- tion may range from several megabytes to hundred megabytes. Thus, network communi- cation takes considerable time in the training process, which reduces system performance. Therefore, many computing architectures have been proposed. This paper proposes a type of distributed computing system for deep learning. Our design aims to reduce synchro- nization times and network blocking times by using a new cache mechanism, called cache pre-forwarding. The design concept of cache pre-forwarding aims to exploit reinforcement learning to train a pre-forwarding policy to increase the cache hit rate. Because of the features of reinforcement learning, our policy is adaptive and applicable to different com- puting environments. Finally, we experimentally demonstrate that our system is feasible. Keywords: Deep learning | Distributed computing | Cache | Reinforcement learning |
مقاله انگلیسی |
20 |
Quasi-pinning synchronization and stabilization of fractional order BAM neural networks with delays and discontinuous neuron activations
هماهنگ سازی شبه پین و تثبیت شبکه های عصبی BAM مرتبه کسری با تاخیر و فعال سازی نورون ناپیوسته-2020 This manuscript concerns quasi-pinning synchronization and β-exponential pinning stabilization for a class of fractional order BAM neural networks with time-varying delays and discontinuous neuron acti- vations (FBAMNNDDAs). Firstly, under the framework of Filippov solution and fractional-order differential inclusions analysis for the initial value problem of FBAMNNDDAs is presented. Secondly, two kinds of novel pinning controllers according to pinning control technique are designed. By means of fractional or- der Lyapunov method and designed pinning control strategy, the sufficient criteria is given first to ensure the quasi-synchronization for the dynamic behavior of FBAMNNDDAs. Furthermore, the error bound of pinning synchronization is explicitly evaluated. Thirdly, via Kakutani s fixed point theorem of set-valued map analysis, Razumikhin condition, and a nonlinear pinning controller, the existence and β-exponential stabilization of FBAMNNDDAs equilibrium point is obtained in the voice of linear matrix inequality (LMI) technique. Fourthly, based on as well as Mittag-Leffler function and growth condition, the global existence of a solution in the Filippov sense of such system is guaranteed with detailed proof. At last, a numerical example with computer simulations are performed to illustrate the effectiveness of proposed theoretical consequences. Keywords: Quasi-synchronization | β-Exponential stabilization | Discontinuous BAM-type neural networks | Fractional order | Time-varying delays | Filippov’s solutions | Pinning control |
مقاله انگلیسی |