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ردیف | عنوان | نوع |
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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.
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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 |