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نتیجه جستجو - Neuron

تعداد مقالات یافته شده: 258
ردیف عنوان نوع
1 Parallel Time-Delay Reservoir Computing With Quantum Dot Lasers
محاسبات مخزن تاخیر زمانی موازی با لیزرهای کوانتومی-2022
A semiconductor laser with optical feedback and optical injection is an appealing scheme to construct the time-delay reservoir computing (TDRC) networks. Quantum dot (QD) lasers are compatible to the silicon platform, and hence is helpful to develop fully on-chip TDRCs. This work theoretically demonstrates a parallel TDRC based on a Fabry-Perot QD laser with multiple longitudinal modes. These modes act as connected physical neurons, which process the input signal in parallel. The interaction strength of the modes is characterized by the cross-gain saturation effect. We show that the neuron interaction strength affects the performance of various benchmark tasks, including the memory capacity, time series prediction, nonlinear channel equalization, and spoken digit recognition. In comparison with the one-channel TDRC with the same number of nodes, the parallel TDRC runs faster and its performance is improved on multiple benchmark tasks.
Index Terms: Reservoir computing | quantum dot lasers | optical neural network | optical feedback | optical injection
مقاله انگلیسی
2 Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization
آموزش طبقه‌بندی‌کننده‌های ترکیبی کلاسیک-کوانتومی از طریق بهینه‌سازی تغییرات تصادفی-2022
Quantum machine learning has emerged as a potential practical application of near-term quantum devices. In this work, we study a two-layer hybrid classical-quantum classifier in which a first layer of quantum stochastic neurons implementing generalized linear models (QGLMs) is followed by a second classical combining layer. The input to the first, hidden, layer is obtained via amplitude encoding in order to leverage the exponential size of the fan-in of the quantum neurons in the number of qubits per neuron. To facilitate implementation of the QGLMs, all weights and activations are binary. While the state of the art on training strategies for this class of models is limited to exhaustive search and single-neuron perceptron-like bit-flip strategies, this letter introduces a stochastic variational optimization approach that enables the joint training of quantum and classical layers via stochastic gradient descent. Experiments show the advantages of the approach for a variety of activation functions implemented by QGLM neurons.
Index Terms: Probabilistic machine learning | quantum computing | quantum machine learning.
مقاله انگلیسی
3 Vision is required for the formation of binocular neurons prior to the classical critical period
بینایی برای تشکیل نورونهای دو چشمی قبل از دوره بحرانی کلاسیک لازم است-2021
Depth perception emerges from the development of binocular neurons in primary visual cortex. Vision is required for these neurons to acquire their mature responses to visual stimuli. The prevailing view is that vision does not influence binocular circuitry until the onset of the critical period, about a week after eye opening, and that plasticity of visual responses is triggered by increased inhibition. Here, we show that vision is required to form binocular neurons and to improve binocular tuning and matching from eye opening until critical period closure. Enhancing inhibition does not accelerate this process. Vision soon after eye opening improves the tuning properties of binocular neurons by strengthening and sharpening ipsilateral eye cortical responses. This progressively changes the population of neurons in the binocular pool, and this plasticity is sensitive to interocular differences prior to critical period onset. Thus, vision establishes binocular circuitry and guides binocular plasticity from eye opening.
مقاله انگلیسی
4 Assessing the neurotoxicity of the carbamate methomyl in Caenorhabditis elegans with a multi-level approach
ارزیابی سمیت عصبی کاربامات متومیل در Caenorhabditis elegans با رویکرد چند سطحی-2021
The neurotoxicity and developmental effects of a widely applied insecticide (methomyl) was investigated by amulti-level approach (behavior and biometry, biochemical alterations and neurodegeneration) in Caenorhabditis elegans upon a short-term exposure (1 h) and a post-exposure period (48 h). The 1-h exposure to sub-lethalconcentrations of methomyl (lower than 0.320 g L—1; i.e. below the estimated LC10) triggered significant changes on motor behavior and development impairment. The type of movement was significantly altered in methomyl-exposed worms, as well as biometric parameters (worms frequently idle and moving more backwards than controls; small body area, length and wavelength). These effects were followed by an increase of acetyl- choline levels. Interestingly, after the 48-h recovery period, movement of previously exposed worms was similar to controls, and a concentration-dependent reversion of biometric endpoints was recorded, pointing out the transient action of the carbamate in line with an apparent absence of cholinergic neurons damage. This study provided new insight on the neurotoxicity of methomyl by showing that effects on movement and development were transient, and apparently did not result in neurodegeneration in cholinergic neurons. Moreover, these findings reinforced the advantages of using C. elegans in a multi-level approach for pesticide effects assessment.
Keywords: Caenorhabditis elegans | Methomyl | Behavior | Acetylcholine | Neurodegeneration
مقاله انگلیسی
5 Printable alginate/gelatin hydrogel reinforced with carbon nanofibers as electrically conductive scaffolds for tissue engineering
هیدروژل آلژینات/ژلاتین قابل چاپ تقویت شده با نانوالیاف کربن به عنوان داربست های رسانای الکتریکی برای مهندسی بافت-2021
Shortages of organs and damaged tissues for transplantation have prompted improvements in biomaterials within the field of tissue engineering (TE). The rise of hybrid hydrogels as electro-conductive biomaterials offers promise in numerous challenging biomedical applications. In this work, hybrid printable biomaterials comprised of alginate and gelatin hydrogel systems filled with carbon nanofibers (CNFs) were developed to create electroconductive and printable 3-D scaffolds. Importantly, the preparation method allows the formation of hydrogels with homogenously dispersed CNFs. These hybrid composite hydrogels were evaluated in terms of mechanical, chemical and cellular response. They display excellent mechanical performance, which is augmented by the CNFs, with Young’s moduli and conductivity reaching 534.7 ± 2.7 kPa and 4.1 × 10− 4 ± 2 × 10− 5 S/cm respectively. CNF incorporation enhances shear-thinning behaviour, allowing ease of 3-D printing. Invitro studies indicate improved cellular proliferation compared to controls. These conductive hydrogels have the potential to be used in a myriad of TE strategies, particularly for those focused on the incorporation of electroconductive components for applications such as cardiac or neuronal TE strategies.
Keywords: Electroactive | Hydrogels | Tissue engineering
مقاله انگلیسی
6 Trajectory smoothing method using reinforcement learning for computer numerical control machine tools
روش هموار سازی مسیر با استفاده از یادگیری تقویتی برای کنترل عددی کامپیوتری ابزارهای ماشینی -2020
Tool-path codes output by computer-aided manufacturing software for high-speed machining are composed of discontinuous G01 line segments. The discontinuity of these tool movements causes computer numerical control (CNC) inefficiency. To achieve high-speed continuous motion, corner smoothing algorithms based on preplanning methods are widely used. However, it is difficult to optimize smoothing trajectories in real-time systems. To obtain smooth trajectories efficiently, this paper proposes a neural network-based direct trajectory smoothing method. An intelligent neural network agent outputs servo commands directly based on the current tool path and running state in every cycle. To achieve direct control, motion feature and reward models were built, and reinforcement learning was used to train the neural network parameters without additional experimental data. The proposed method provides higher cutting efficiency than the local and global smoothing algorithms. Given its simple structure and low computational demands, it can easily be applied to real-time CNC systems.
Keywords: CNC | Trajectory smoothing | Neuron network | Reinforcement learning
مقاله انگلیسی
7 Dynamic resource allocation during reinforcement learning accounts for ramping and phasic dopamine activity
تخصیص منابع پویا در طول حساب های یادگیری تقویتی برای فعالیت دوپامین ramping و مرحله ای-2020
For an animal to learn about its environment with limited motor and cognitive resources, it should focus its resources on potentially important stimuli. However, too narrow focus is disadvantageous for adaptation to environmental changes. Midbrain dopamine neurons are excited by potentially important stimuli, such as reward-predicting or novel stimuli, and allocate resources to these stimuli by modulating how an animal approaches, exploits, explores, and attends. The current study examined the theoretical possibility that dopamine activity reflects the dynamic allocation of resources for learning. Dopamine activity may transition between two patterns: (1) phasic responses to cues and rewards, and (2) ramping activity arising as the agent approaches the reward. Phasic excitation has been explained by prediction errors generated by experimentally inserted cues. However, when and why dopamine activity transitions between the two patterns remain unknown. By parsimoniously modifying a standard temporal difference (TD) learning model to accommodate a mixed presentation of both experimental and environmental stimuli, we simulated dopamine transitions and compared them with experimental data from four different studies. The results suggested that dopamine transitions from ramping to phasic patterns as the agent focuses its resources on a small number of rewardpredicting stimuli, thus leading to task dimensionality reduction. The opposite occurs when the agent re-distributes its resources to adapt to environmental changes, resulting in task dimensionality expansion. This research elucidates the role of dopamine in a broader context, providing a potential explanation for the diverse repertoire of dopamine activity that cannot be explained solely by prediction error.
Keywords: Prediction error | Salience | Temporal-difference learning model | Pearce-Hall model | Habit | Striatum
مقاله انگلیسی
8 NeuronFlow: a neuromorphic processor architecture for Live AI applications
NeuronFlow: معماری پردازنده نورومورفیک برای برنامه های کاربردی هوش مصنوعی زنده -2020
Neuronflow is a neuromorphic, many core, data flow architecture that exploits brain-inspired concepts to deliver a scalable event-based processing engine for neuron networks in Live AI applications. Its design is inspired by brain biology, but not necessarily biologically plausible. The main design goal is the exploitation of sparsity to dramatically reduce latency and power consumption as required by sensor processing at the Edge.
مقاله انگلیسی
9 A different sleep apnea classification system with neural network based on the acceleration signals
یک سیستم طبقه بندی sleep apnea متفاوت با شبکه عصبی مبتنی بر سیگنال های شتاب-2020
Background and objective: The apnea syndrome is characterized by an abnormal breath pause or reduction in the airflow during sleep. It is reported in the literature that it affects 2% of middle-aged women and 4% of middle-aged men, approximately. This study has vital importance, especially for the elderly, the disabled, and pediatric sleep apnea patients. Methods: In this study, a new diagnostic method is developed to detect the apnea event by using a microelectromechanical system (MEMS) based acceleration sensor. It records the value of acceleration by measuring the movements of the diaphragm in three axes during the respiratory. The measurements are carried out simultaneously, a medical spirometer (Fukuda Sangyo), to test the validity of measurement results. An artificial neural network model was designed to determine the apnea event. For the number of neurons in the hidden layer, 1-3-5-10-18-20-25 values were tried, and the network with three hidden neurons giving the most suitable result was selected. In the designed ANN, three layers were formed that three neurons in the hidden layer, the two neurons at the input, and two neurons at the output layer. Results: A study group was formed of 5 patients (having different characteristics (age, height, and body weight)). The patients in the study group have sleep apnea (SA) in different grades. Several 12.723 acceleration data (ACC) in the XYZ-axis from 5 different patients are recorded for apnea event training and detection. The measured accelerometer (ACC) data from one of the patients (called H1) are used to train an ANN. During the training phase, MSE is used to calculate the fitness value of the apnea event. Then Apnea event is detected successfully for the other patients by using ANN trained only with H1’s ACC data. Conclusions: The sleep apnea event detection system is presented by using ANN from directly acceleration values. Measurements are performed by the MEMS-based accelerometer and Industrial Spirometer simultaneously. A total of 12723 acceleration data is measured from 5 different patients. The best result in 7000 iterations was reached (the number of iterations was tried up to 10.000 with 1000 steps). 605 data of only H1 measurements are used to train ANN, and then all data used to check the performance of the ANN as well as H2, H3, H4, and H5 measurement results. MSE performance benchmark shows us that trained ANN successfully detects apnea events. One of the contributions of this study to literature is that only ACC data are used in the ANN training step. After training for one patient, the ANN system can monitor the apnea event situation on-line for others.
Keywords: Sleep apnea | Acceleration sensor | Acceleration data | Artificial neural network | Medical decision making
مقاله انگلیسی
10 The Negative Affect of Protracted Opioid Abstinence: Progress and Perspectives From Rodent Models
تأثیر منفی پرهیز از مصرف مواد افیونی طولانی: پیشرفت و چشم انداز مدل های جوندگان-2020
Opioid use disorder (OUD) is characterized by the development of a negative emotional state that develops after a history of long-term exposure to opioids. OUD represents a true challenge for treatment and relapse prevention. Human research has amply documented emotional disruption in individuals with an opioid substance use disorder, at both behavioral and brain activity levels; however, brain mechanisms underlying this particular facet of OUD are only partially understood. Animal research has been instrumental in elucidating genes and circuits that adapt to long-term opioid use or are modified by acute withdrawal, but research on long-term consequences of opioid exposure and their relevance to the negative affect of OUD remains scarce. In this article, we review the literature with a focus on two questions: 1) Do we have behavioral models in rodents, and what do they tell us? and 2) What do we know about the neuronal populations involved? Behavioral rodent models have successfully recapitulated behavioral signs of the OUD-related negative affect, and several neurotransmitter systems were identified (i.e., serotonin, dynorphin, corticotropin-releasing factor, oxytocin). Circuit mechanisms driving the negative mood of prolonged abstinence likely involve the 5 main reward–aversion brain centers (i.e., nucleus accumbens, bed nucleus of the stria terminalis, amygdala, habenula, and raphe nucleus), all of which express mu opioid receptors and directly respond to opioids. Future work will identify the nature of these mu opioid receptor–expressing neurons throughout reward–aversion networks, characterize their adapted phenotype in opioid abstinent animals, and hopefully position these primary events in the broader picture of mu opioid receptor–associated brain aversion networks.
Keywords: Mood | Mu opioid receptor (MOR) | Neural circuits | Opioid use disorder (OUD) | Opioid withdrawal | Rodent behavior
مقاله انگلیسی
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