Neural Components Research Articles

Aerodynamics and motor control of ultrasonic vocalizations for social communication in mice and rats

BackgroundRodent ultrasonic vocalizations (USVs) are crucial to their social communication and a widely used translational tool for linking gene mutations to behavior. To maximize the causal interpretation of experimental treatments, we need to understand how neural control affects USV production. However, both the aerodynamics of USV production and its neural control remain poorly understood.ResultsHere, we test three intralaryngeal whistle mechanisms—the wall and alar edge impingement, and shallow cavity tone—by combining in vitro larynx physiology and individual-based 3D airway reconstructions with fluid dynamics simulations. Our results show that in the mouse and rat larynx, USVs are produced by a glottal jet impinging on the thyroid inner wall. Furthermore, we implemented an empirically based motor control model that predicts motor gesture trajectories of USV call types.ConclusionsOur results identify wall impingement as the aerodynamic mechanism of USV production in rats and mice. Furthermore, our empirically based motor control model shows that both neural and anatomical components contribute to USV production, which suggests that changes in strain specific USVs or USV changes in disease models can result from both altered motor programs and laryngeal geometry. Our work provides a quantitative neuromechanical framework to evaluate the contributions of brain and body in shaping USVs and a first step in linking descending motor control to USV production.

Clinical applications of personalising the neural components of visual image quality metrics for individual eyes.

Eyecare is evolving increasingly personalised corrections and increasingly personalised evaluations of corrections on-eye. This report describes individualising optical and neural components of the VSX (visual Strehl) metric and evaluates personalisation using two clinical applications. (1) Better understanding visual experience: While VSX tracks visual performance in typical eyes, non-individualised metrics underestimated visual performance in highly aberrated eyes - could this be understood by personalising metrics? (2) Metric-optimised objective spherocylindrical refractions in typical and atypical eyes have used neural weighting functions of typical eyes - will personalisation affect the outcome in clinical 0.25D steps? Orientation-specific neural contrast sensitivity was measured in four typical myopic and astigmatic eyes and six eyes with keratoconus. Wavefront error was measured in all eyes while uncorrected and when the keratoconic eyes wore wavefront-guided scleral lenses. Total experiment duration was 24-28h per subject. Two versions of VSX were calculated for each application: one weighted ocular optics with measured neural contrast sensitivity data from that eye, another weighted optics with a representative neural function of typical eyes. Wavefront-guided corrections were evaluated using the two metric values. Spherocylindrical corrections that optimised each metric were identified. Metric values for keratoconic eyes improved by a mean factor of 1.99 (~0.3log units) when personalised. Applying this factor to a larger sample of eyes from a previous keratoconus study reconciled dissonances between the percentage of eyes reaching normative best-corrected metric levels and the percentages of eyes reaching normative levels of visual acuity and contrast sensitivity. Spherocylindrical corrections that optimised both versions of VSX were clinically equivalent (mean±SD Euclidean dioptric difference 0.13±0.18 D). Personalising visual image quality metrics is beneficial when actual metric values are used (evaluating ophthalmic corrections on-eye against norms) and when fine increments in visual quality are imparted (wavefront-guided corrections). However, partially individualised metrics appear adequate when metrics relatively rank spherocylindrical corrections in 0.25 D steps.

Designing a 3D Printed Model of the Skull-Base: A Collaboration Between Clinicians and Industry

IntroductionThe role of three dimensional (3D) printing in neurosurgical education is becoming increasingly common. Notably, 3D printing can simulate complex anatomical pathways that may be difficult to regularly and accurately reproduce in cadavers. One such example is the course of the facial nerve within the temporal bone and its relation to the labyrinth. This can aid pre-surgical planning and minimise surgical complications. Here we aim to develop a novel anatomically accurate model of the skull base which demonstrates key neuro vascular components and the course of the facial nerve within the temporal bone by developing a 3D printed model of the skull-base that can be used for medical education and pre-surgical planning.Materials and MethodsWe utilised a combination of Computed Tomography (CT) and angiography scans to reconstruct the skull base and its vascular contents. Neural components were digitally incorporated under the guidance of the Oxford neurosurgical team and the anatomy department. The model was integrated and printed using polymer jetting.ResultsThe model was successfully printed, with all neurovascular components included. Notably we were able to highlight the intra-temporal course of the facial nerve by creating a bony window within the temporal bone.ConclusionThrough a collaboration with industry and a multidisciplinary team, we were able to reproduce the base of the skull from patient neuro-imaging. Our model is both cost-effective, reproducible and can aid both medical students and neurosurgical trainees in their training/education.

GlauNet: Glaucoma Diagnosis for OCTA Imaging Using a New CNN Architecture

Glaucoma is a neurodegenerative disease that affects the optic nerve head and causes visual field defect. Current investigations focus on neural component which may overlook other important factors such as the vascular cause. The optical coherence tomography angiography (OCTA) imaging has been developed and provided quantitative parameters that showed good diagnostic accuracy to detect glaucoma. However, those parameters are based on image processing of observed clinical findings, therefore, some image information can be lost. Convolutional neural network has been successfully applied for automatic feature extraction and object classification. In this study, the glaucoma diagnosis network, namely GlauNet, has been proposed. GlauNet consists of two sections: the feature-extraction section and the classification section. The feature-extraction section has three convolutional layers. Each convolutional layer is followed by rectified linear unit and maximum pooling layer. The classification section contains five fully connected layers. GlauNet was trained with 258 glaucomatous and 439 non-glaucomatous eyes. The visualization of the feature-extraction section showed the highlight in the area of optic nerve head and retinal nerve fiber layer in the superotemporal and inferotemporal regions. It was then tested on 27 glaucomatous and 48 non-glaucomatous eyes. Its sensitivity and specificity were 88.9% with 89.6%, respectively. The area under receiver operating characteristic curve of GlauNet was 0.89. GlauNet was robust against the artifacts. Its sensitivity and specificity were still higher than 80% (82.4% and 80.3%, respectively) when tested on 88 poor-quality images.

Malignant Ectomesenchymoma of Pelvis in an Infant

Malignant ectomesenchymoma is a rare pediatric soft tissue tumor that contains both neuroectodermal and mesenchymal components. It is frequently found in the pelvis, paratesticular soft tissue, external genitalia and the head-neck region. We present a 12-month-old boy with a recurrent pelvic tumor excised 5 months ago and diagnosed as neuroblastoma. Histological consultation of the initial tumor tissue revealed a biphasic pattern with rhabdomyosarcomatous and neural components. The mesenchymal component was embryonal rhabdomyosarcoma showing desmin, myogenin, and Myo-D1 positivity. The neuroectodermal component was mainly Schwannian stroma stained with S100 antibody and scattered synaptophysin and chromogranin positive ganglion cells were also present. The patient was diagnosed with malignant ectomesenchymoma. A chemotherapy regime was started and surgical approach to the recurrent tumor was planned. We present here a pediatric case of malignant ectomesenchymoma because of its rarity and it is important that this entity should be considered in the differential diagnosis for appropriate clinical management.

Chapter 27 - Plasticity of the language system in children and adults

The language system is perhaps the most unique feature of the human brain's cognitive architecture. It has long been a quest of cognitive neuroscience to understand the neural components that contribute to the hierarchical pattern processing and advanced rule learning required for language. The most important goal of this research is to understand how language becomes impaired when these neural components malfunction or are lost to stroke, and ultimately how we might recover language abilities under these circumstances. Additionally, understanding how the language system develops and how it can reorganize in the face of brain injury or dysfunction could help us to understand brain plasticity in cognitive networks more broadly. In this chapter we will discuss the earliest features of language organization in infants, and how deviations in typical development can-but in some cases, do not-lead to disordered language. We will then survey findings from adult stroke and aphasia research on the potential for recovering language processing in both the remaining left hemisphere tissue and in the non-dominant right hemisphere. Altogether, we hope to present a clear picture of what is known about the capacity for plastic change in the neurobiology of the human language system.

Revealing the Semantics of Data Wrangling Scripts With COMANTICS.

Data workers usually seek to understand the semantics of data wrangling scripts in various scenarios, such as code debugging, reusing, and maintaining. However, the understanding is challenging for novice data workers due to the variety of programming languages, functions, and parameters. Based on the observation that differences between input and output tables highly relate to the type of data transformation, we outline a design space including 103 characteristics to describe table differences. Then, we develop COMANTICS, a three-step pipeline that automatically detects the semantics of data transformation scripts. The first step focuses on the detection of table differences for each line of wrangling code. Second, we incorporate a characteristic-based component and a Siamese convolutional neural network-based component for the detection of transformation types. Third, we derive the parameters of each data transformation by employing a "slot filling" strategy. We design experiments to evaluate the performance of COMANTICS. Further, we assess its flexibility using three example applications in different domains.

NAS-Navigator: Visual Steering for Explainable One-Shot Deep Neural Network Synthesis.

The success of DL can be attributed to hours of parameter and architecture tuning by human experts. Neural Architecture Search (NAS) techniques aim to solve this problem by automating the search procedure for DNN architectures making it possible for non-experts to work with DNNs. Specifically, One-shot NAS techniques have recently gained popularity as they are known to reduce the search time for NAS techniques. One-Shot NAS works by training a large template network through parameter sharing which includes all the candidate NNs. This is followed by applying a procedure to rank its components through evaluating the possible candidate architectures chosen randomly. However, as these search models become increasingly powerful and diverse, they become harder to understand. Consequently, even though the search results work well, it is hard to identify search biases and control the search progression, hence a need for explainability and human-in-the-loop (HIL) One-Shot NAS. To alleviate these problems, we present NAS-Navigator, a visual analytics (VA) system aiming to solve three problems with One-Shot NAS; explainability, HIL design, and performance improvements compared to existing state-of-the-art (SOTA) techniques. NAS-Navigator gives full control of NAS back in the hands of the users while still keeping the perks of automated search, thus assisting non-expert users. Analysts can use their domain knowledge aided by cues from the interface to guide the search. Evaluation results confirm the performance of our improved One-Shot NAS algorithm is comparable to other SOTA techniques. While adding Visual Analytics (VA) using NAS-Navigator shows further improvements in search time and performance. We designed our interface in collaboration with several deep learning researchers and evaluated NAS-Navigator through a control experiment and expert interviews.

Modeling and Analysis of the Intrinsic Cardiac Nervous System in Closed-Loop Cardiovascular Control

The baroreceptor reflex is a multi-input, multi-output physiological control system that regulates short-term blood pressure by modulating nerve activity between the brainstem and the heart. The computational model by Park et al. (2020) is the most recent iteration in our exploration of the system. However, the contributions of ”the little brain of the heart”, the intrinsic cardiac nervous system (ICN), to local control of the heart and to the integration of sensory information is unknown and has been overlooked in previous models. We have incorporated a high-fidelity representation of the ICN into a model of the baroreceptor reflex based on anatomical, molecular, and physiological evidence. The model consists of (1) differential equations to represent the cardiovascular system, and (2) transfer functions to represent neural control components, connected in a closed-loop control circuit. We use the model to evaluate the impact of alternative ICN network structures on overall cardiovascular control in response to mean arterial pressure and lung tidal volume perturbations. Our results show that the local circuit neurons that integrate sensory information into the ICN strengthen the response of ICN neuron activity, especially at low blood pressures, suggesting that the ICN amplifies the brainstem's response to perturbations.

Proportional integral derivative booster for neural networks-based time-series prediction: Case of water demand prediction

Multi-step time-series prediction is an essential supportive step for decision-makers in several industrial areas. Artificial intelligence techniques, which use a neural network component in various forms, have recently frequently been used to accomplish this step. However, the complexity of the neural network structure still stands up as a critical problem against prediction accuracy. In this paper, a method inspired by the proportional–integral–derivative (PID) control approach is investigated to enhance the performance of neural network models used for multi-step ahead prediction of periodic time-series information while maintaining a negligible impact on the complexity of the system. The PID-based method is applied to the predicted value at each time step to bring that value closer to the real value. The water demand forecasting problem is considered as a case study, where two deep neural network models from the literature are used to prove the effectiveness of the proposed boosting method. Furthermore, to prove the applicability of this PID-based booster to other types of periodic time-series prediction problems, it is applied to enhance the accuracy of a neural network model used for multi-step forecasting of hourly energy consumption. The comparison between the results of the original prediction models and the results after using the proposed technique demonstrates the superiority of the proposed method in terms of prediction accuracy and system complexity.

Real-time processes in the development of action planning

Across species and ages, planning multi-step actions is a hallmark of intelligence and critical for survival. Traditionally, researchers adopt a "top-down" approach to action planning by focusing on the ability to create an internal representation of the world that guides the next step in a multi-step action. However, a top-down approach does not inform on underlying mechanisms, so researchers can only speculate about how and why improvements in planning occur. The current study takes a "bottom-up" approach by testing developmental changes in the real-time, moment-to-moment interplay among perceptual, neural, and motor components of action planning using simultaneous video, motion-tracking, head-mounted eye tracking, and electroencephalography (EEG). Preschoolers (n= 32) and adults (n= 22) grasped a hammer with their dominant hand to pound a peg when the hammer handle pointed in different directions. When the handle pointed toward their non-dominant hand, younger children ("nonadaptive planners") used a habitual overhand grip that interfered with wielding the hammer, whereas adults and older children ("adaptive planners") used an adaptive underhand grip. Adaptive and nonadaptive children differed in when and where they directed their gaze to obtain visual information, neural activation of the motor system before reaching, and straightness oftheir reach trajectories. Nonadaptive children immediately used a habitual overhand grip before gathering visual information, leaving insufficient time to form a plan before acting. Our novel bottom-up approach transcends mere speculation by providing converging evidence that the development of action planning depends on a real-time "tug of war" between habits and information gathering and processing.

Modeling of the processes of extracting rules from Neural Network components with Petri nets

The paper deals with the use of extended Petri nets in modeling the processes of extracting rules from neural network components. The mathematical model for extracting rules from neural network components based on a modified timed Petri net is constructed, followed by an analysis of its dynamic behavior based on a timed reachability graph, which is a set of all its states that can be reached when a finite number of transitions are fired. The proposed model allows us to move from the initial detailed structure to its simplified description, which preserves the possibility of obtaining information about the structure and dynamic behavior of the neural network system. The proposed approach can be used in the synthesis of cognitive systems with a neural network organization to provide computational support for the functions of forming, learning, and correcting cognitive networks that display neural network models.

RESTING STATE FUNCTIONAL CONNECTIVITY CHANGES IN NATURALISTIC STIMULATION STUDY

Naturalistic stimulation has had increasing influence on cognitive neuroscience in the last decade. Several studies have used naturalistic stimulation to investigate the connectivity dynamics under pathological condition or in healthy children for studying the developing brain. However, little is known about the connectivity changes during watching a movie in healthy, adult population. Our aim is to delineate the potential dynamic connectivity pattern of the healthy, mature brain during naturalistic movie watching compared to complete rest. In an observational case series, e leven healthy adult volunteers with no history of psychiatric or neurological illness underwent two magnetic resonance echo-planar imaging acquisitions. During the first acquisition they were asked to rest with their eyes closed and during the subsequent acquisition they watched a movie clip with slow human body movements. The Independent Component Analysis provided neural components which were further submitted to intra- and inter-network analyses. We found significantly increased intra-network connectivity during movie watching for the extrastriate visual and for the posterior visual networks. The inter-network analysis showed that for the extravisual and primary visual pair, the connectivity was lower during movie watching compared to rest condition. We also found significant uncoupling between the anterior Default Mode Network and the secondary somatosensory network during movie watching. These findings suggest greater visual network segregation during movie watching in order to support processing of complex stimuli. The decoupling of the secondary somatosensory network from the anterior Default Mode Network during movie watching suggest a somatosensory function segregation and its important role in processing information from observing the naturalistic movements of others. Brain imaging measures of major brain networks can contribute to early identification of risk for common psychiatric disorders hence facilitating preventive therapy. Keywords: naturalistic stimulation , rest, conectivity Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Times New Roman; mso-bidi-theme-font:minor-bidi; mso-ansi-language:EN-US; mso-fareast-language:EN-US;}

Considering Neural Connectivity in Point Process Decoder for Brain-Machine Interface.

Brain machine interface (BMI) can translate neural activity into digital commands to control prostheses. The decoder in BMI models the mechanism relating to neural activity and intents in brain. In our brain, single neuronal tuning property and neural connectivity contribute to encoding the intents together. These properties may change, a phenomenon which is named neural adaptation during using BMIs. Neural adaptation requires the decoder to consider the two factors at the same time and has the potential to follow their changes. However, in the previous work, the class of neural network and clustering decoder can consider the neural connectivity regardless of the single neuronal tuning property. On the other hand, point process methods can model the single neuronal tuning property but fail to address the neural connectivity. In this paper, we propose a new point process decoder with the information of neural connectivity named NCPP. We derive the neural connectivity component from the point process method by Bayes' rule and use a clustering decoder to represent the neural connectivity. This method can consider the neural connectivity and the single neuronal tuning property at the same time. We validate the method on simulation data where the point process method cannot achieve a good decoding performance and compare it with sequential Monte Carlo point process method (SMCPP). The results show our method outperforms the pure point process method which indicates our method can model the neural connectivity and single neuronal tuning property at the same time.Clinical Relevance-This paper proposes a decoder that can model the neural connectivity and the single neuronal tuning property at the same time, which is potential to explain the neural adaptation computationally.

Mitigating the Impact of Psychophysical Effects During Adaptive Stimulus Selection in the P300 Speller Brain-Computer Interface.

Stimulus-driven brain-computer interfaces (BCIs), such as the P300 speller, rely on using sensory stimuli to elicit specific neural signal components called event-related potentials (ERPs) to control external devices. However, psychophysical factors, such as refractory effects and adjacency distractions, may negatively impact ERP elicitation and BCI performance. Although conventional BCI stimulus presentation paradigms usually design stimulus presentation schedules in a pseudo-random manner, recent studies have shown that controlling the stimulus selection process can enhance ERP elicitation. In prior work, we developed an algorithm to adaptively select BCI stimuli using an objective criterion that maximizes the amount of information about the user's intent that can be elicited with the presented stimuli given current data conditions. Here, we enhance this adaptive BCI stimulus selection algorithm to mitigate adjacency distractions and refractory effects by modeling temporal dependencies of ERP elicitation in the objective function and imposing spatial restrictions in the stimulus search space. Results from simulations using synthetic data and human data from a BCI study show that the enhanced adaptive stimulus selection algorithm can improve spelling speeds relative to conventional BCI stimulus presentation paradigms.Clinical relevance-Increased communication rates with our enhanced adaptive stimulus selection algorithm can potentially facilitate the translation of BCIs as viable communication alternatives for individuals with severe neuromuscular limitations.

Short-training Algorithm for Online Brain-machine Interfaces Using One-photon Microendoscopic Calcium Imaging.

Calcium imaging has great potential to be applied to online brain-machine interfaces (BMIs). As opposed to two-photon imaging settings, a one-photon microendoscopic imaging device can be chronically implanted and is subject to little motion artifacts. Traditionally, one-photon microendoscopic calcium imaging data are processed using the constrained nonnegative matrix factorization (CNMFe) algorithm, but this batched processing algorithm cannot be applied in real-time. An online analysis of calcium imaging data algorithm (or OnACIDe) has been proposed, but OnACIDe updates the neural components by repeatedly performing neuron identification frame-by-frame, which may decelerate the update speed if applying to online BMIs. For BMI applications, the ability to track a stable population of neurons in real-time has a higher priority over accurately identifying all the neurons in the field of view. By leveraging the fact that 1) microendoscopic recordings are rather stable with little motion artifacts and 2) the number of neurons identified in a short training period is sufficient for potential online BMI tasks such as cursor movements, we proposed the short-training CNMFe algorithm (stCNMFe) that skips motion correction and neuron identification processes to enable a more efficient BMI training program in a one-photon microendoscopic setting.

Heat Stress Responses in Birds: A Review of the Neural Components

Simple SummaryHeat stress is a major environmental condition negatively impacting the wellbeing of various avian species. In chickens, heat exposure is associated with disruption of metabolic and immune system function, and an increased risk of mortality. This has a negative impact on the food economy, as chicken products make up roughly 34% of the world’s protein. Techniques to mitigate exposure to high temperatures have been discussed in depth, and most research suggests that the root cause of heat stress-induced physiological aberrations is alterations in the stress response and reduced food intake. Unfortunately, little is known about thermoregulation in birds. That thermoregulation, food intake, and the stress response are all mediated by the hypothalamus make it tempting to speculate that it is the central hub at which these systems interact and signals from diverse pathways are integrated. Thus, this review discusses the neural circuitry in birds associated with thermoregulation, food intake, and stress response at the level of the hypothalamus, with a focus on how these systems might interact in the presence of heat exposure.Heat stress is one of the major environmental conditions causing significant losses in the poultry industry and having negative impacts on the world’s food economy. Heat exposure causes several physiological impairments in birds, including oxidative stress, weight loss, immunosuppression, and dysregulated metabolism. Collectively, these lead not only to decreased production in the meat industry, but also decreases in the number of eggs laid by 20%, and overall loss due to mortality during housing and transit. Mitigation techniques have been discussed in depth, and include changes in air flow and dietary composition, improved building insulation, use of air cooling in livestock buildings (fogging systems, evaporation panels), and genetic alterations. Most commonly observed during heat exposure are reduced food intake and an increase in the stress response. However, very little has been explored regarding heat exposure, food intake and stress, and how the neural circuitry responsible for sensing temperatures mediate these responses. That thermoregulation, food intake, and the stress response are primarily mediated by the hypothalamus make it reasonable to assume that it is the central hub at which these systems interact and coordinately regulate downstream changes in metabolism. Thus, this review discusses the neural circuitry in birds associated with thermoregulation, food intake, and stress response at the level of the hypothalamus, with a focus on how these systems might interact in the presence of heat exposure.

Quantifying neural and non-neural components of wrist hyper-resistance after stroke: Comparing two instrumented assessment methods

Patients with poor upper limb motor recovery after stroke are likely to develop increased resistance to passive wrist extension, i.e., wrist hyper-resistance. Quantification of the underlying neural and non-neural elastic components is of clinical interest. This cross-sectional study compared two methods: a commercially available device (NeuroFlexor®) with an experimental EMG-based device (Wristalyzer) in 43 patients with chronic stroke. Spearman's rank correlation coefficients (r) between components, modified Ashworth scale (MAS) and range of passive wrist extension (PRoM) were calculated with 95% confidence intervals. Neural as well as elastic components assessed by both devices were associated (r = 0.61, 95%CI: 0.38-0.77 and r = 0.53, 95%CI: 0.28–0.72, respectively). The neural component assessed by the NeuroFlexor® associated significantly with the elastic components of NeuroFlexor® (r = 0.46, 95%CI: 0.18–0.67) and Wristalyzer (r = 0.36, 95%CI: 0.06–0.59). The neural component assessed by the Wristalyzer was not associated with the elastic components of both devices. Neural and elastic components of both devices associated similarly with the MAS (r = 0.58, 95%CI: 0.34–0.75 vs. 0.49, 95%CI: 0.22–0.69 and r = 0.51, 95%CI: 0.25–0.70 vs. 0.30, 95%CI: 0.00–0.55); elastic components associated with PRoM (r = -0.44, 95%CI: -0.65- -0.16 vs. -0.74, 95%CI: -0.85- -0.57 for NeuroFlexor® and Wristalyzer respectively). Results demonstrate that both methods perform similarly regarding the quantification of neural and elastic wrist hyper-resistance components and have an added value when compared to clinical assessment with the MAS alone. The added value of EMG in the discrimination between neural and non-neural components requires further investigation.

Prediction Strategies of Stock Market Data Using Deep Learning Algorithm

Background: Predictive analytics has a multiplicity of statistical schemes from predictive modelling, data mining, machine learning. It scrutinizes present and chronological data to make predictions about expectations or if not unexplained measures. Most predictive models are used for business analytics to overcome loses and profit gaining. Predictive analytics is used to exploit the pattern in old and historical data. Objective: People used to follow some strategies for predicting stock value to invest in the more profit-gaining stocks and those strategies to search the stock market prices which are incorporated in some intelligent methods and tools. Such strategies will increase the investor’s profits and also minimize their risks. So prediction plays a vital role in stock market gaining and is also a very intricate and challenging process. Method: The proposed optimized strategies are the Deep Neural Network with Stochastic Gradient for stock prediction. The Neural Network is trained using Back-propagation neural networks algorithm and stochastic gradient descent algorithm as optimal strategies. Results: The experiment is conducted for stock market price prediction using python language with the visual package. In this experiment RELIANCE.NS, TATAMOTORS.NS, and TATAGLOBAL. NS dataset are taken as input dataset and it is downloaded from National Stock Exchange site. The artificial neural network component including Deep Learning model is most effective for more than 100,000 data points to train this model. This proposed model is developed on daily prices of stock market price to understand how to build model with better performance than existing national exchange method.

Neural ranking models for document retrieval

Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-crafted features. Recently, researchers have leveraged deep learning models in information retrieval. These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations of hand-crafted features. A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking. In this paper, we compare the proposed models in the literature along different dimensions in order to understand the major contributions and limitations of each model. In our discussion of the literature, we analyze the promising neural components, and propose future research directions. We also show the analogy between document retrieval and other retrieval tasks where the items to be ranked are structured documents, answers, images and videos.