Leg rigidity is associated with frequent falls in people with Parkinson’s disease, suggesting a potential role in functional balance and gait impairments. Changes in the neural state due to secondary tasks, e.g., activation maneuvers, can exacerbate rigidity, possibly increasing the risk of falls. However, the subjective interpretation and coarse classification of the standard clinical rigidity scale has prohibited the systematic, objective assessment of resting and activated leg rigidity. The pendulum test is an objective diagnostic method that we hypothesized would (...) be sensitive enough to characterize resting and activated leg rigidity. We recorded kinematic data and electromyographic signals from rectus femoris and biceps femoris during the pendulum test in 15 individuals with PD, spanning a range of leg rigidity severity. From the recorded data of leg swing kinematics, we measured biomechanical outcomes including first swing excursion, first extension peak, number and duration of the oscillations, resting angle, relaxation index, maximum and minimum angular velocity. We examined associations between biomechanical outcomes and clinical leg rigidity score. We evaluated the effect of increasing rigidity through activation maneuvers on biomechanical outcomes. Finally, we assessed whether either biomechanical outcomes or changes in outcomes with activation were associated with a fall history. Our results suggest that the biomechanical assessment of the pendulum test can objectively quantify parkinsonian leg rigidity. We found that the presence of high rigidity during clinical exam significantly impacted biomechanical outcomes, i.e., first extension peak, number of oscillations, relaxation index, and maximum angular velocity. No differences in the effect of activation maneuvers between groups with clinically assessed low rigidity were observed, suggesting that activated rigidity may be independent of resting rigidity and should be scored as independent variables. Moreover, we found that fall history was more common among people whose rigidity was increased with a secondary task, as measured by biomechanical outcomes. We conclude that different mechanisms contributing to resting and activated rigidity may play an important yet unexplored functional role in balance impairments. The pendulum test may contribute to a better understanding of fundamental mechanisms underlying motor symptoms in PD, evaluating the efficacy of treatments, and predicting the risk of falls. (shrink)

Transmembrane receptor tyrosine kinases that bind to peptide factors transmit essential growth and differentiation signals. A growing list of orphan receptors, of which some are oncogenic, holds the promise that many unknown ligands may be discovered by tracking the corresponding surface molecules. The neu gene (also called erbB‐2 and HER‐2) encodes such a receptor tyrosine kinase whose oncogenic potential is released in the developing rodent nervous system through a point mutation. Amplification and overexpression of neu are thought to contribute (...) to malignancy of certain human adenocarcinomas. The search for soluble factors that interact with the Neu receptor led to the discovery of a 44 kDa glyco‐protein that induces phenotypic differentiation of cultured mammary tumor cells to growth‐arrested and milk‐producing cells. The Neu differentiation factor (NDF or heregulin), however, also acts as a mitogen for epithelial, Schwann and glial cells. Multiple forms of the factor are produced by alternative splicing and their expression is confined predominantly to the central and to the peripheral nervous systems. One identified neuronal function of this family of polypeptides is to control the formation of neuromuscular junctions, but their physiological role in secretory epithelia is still unknown. Other open questions relate to the transmembrane topology of various precursors, the identity of a putative co‐receptor, the possible existence of additional ligands of Neu and the functional significance of the interaction between Neu and at least three highly related receptor tyrosine kinases. (shrink)

We present a novel method, called graph sparse nonnegative matrix factorization, for dimensionality reduction. The affinity graph and sparse constraint are further taken into consideration in nonnegative matrix factorization and it is shown that the proposed matrix factorization method can respect the intrinsic graph structure and provide the sparse representation. Different from some existing traditional methods, the inertial neural network was developed, which can be used to optimize our proposed matrix factorization problem. By adopting one parameter in the (...) class='Hi'>neural network, the global optimal solution can be searched. Finally, simulations on numerical examples and clustering in real-world data illustrate the effectiveness and performance of the proposed method. (shrink)

How different neural crest derivatives differentiate in distinct embryonic locations in the vertebrate embryo is an intriguing issue. Many attempts have been made to understand the underlying mechanism of specific pathway choices made by migrating neural crest cells. In this speculative review we suggest a new mechanism for the regulation of neural crest cell migration patterns in avian and mammalian embryos, based on recent progress in understanding the expression and activity of receptor tyrosine kinases during embryogenesis. Distinct (...) subpopulations of crest‐derived cells express specific receptor tyrosine kinases while residing in a migration staging area. We postulate that the differential expression of receptor tyrosine kinases by specific subpopulations of neural crest cells allows them to respond to localized growth factor ligand activity in the embryo. Thus, the migration pathway taken by neural crest subpopulations is determined by their receptor tyrosine kinase response to the differential localization of their cognate ligand. (shrink)

Lindquist et al. assess the neural evidence for locationist versus psychological construction accounts of human emotion. A wealth of experimental and clinical investigations show that individual differences in emotion and personality influence emotion processing. These factors may also influence the brain's response to emotional stimuli. A synthesis of the relevant neuroimaging data must therefore take these factors into consideration.

With a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. The Elman recurrent network is renowned for its capability of dealing with dynamic information, which has made it a successful application to predicting. We developed a hybrid approach which combined Elman recurrent network with factorization machine technique, i.e., the FM-Elman neural network, to predict stock market volatility. (...) In this paper, the Standard & Poor’s 500 Composite Stock Price index, the Dow Jones industrial average index, the Shanghai Stock Exchange Composite index, and the Shenzhen Securities Component Index were used to demonstrate the validity of our proposed FM-Elman model in time-series prediction. The results were compared with predictions obtained from the other two models which are basic BP neural network and the Elman neural network. Some experiments showed that the FM-Elman model outperforms others through different accuracy measures. Furthermore, the effects of volatility degree on prediction performance from different stock indexes were investigated. An interesting phenomenon had been found through some numerical experiments on the effects of different user-specified dimensions on the proposed FM-Elman neural network. (shrink)

This research aims to analyze the influencing factors of migrant children’s education integration based on the convolutional neural network algorithm. The attention mechanism, LSTM, and GRU are introduced based on the CNN algorithm, to establish an ALGCNN model for text classification. Film and television review data set, Stanford sentiment data set, and news opinion data set are used to analyze the classification accuracy, loss value, Hamming loss, precision, recall, and micro-F1 of the ALGCNN model. Then, on the big (...) data platform, data in the Comprehensive Management System of Floating Population and Rental Housing, Student Status Information Management System, and Student Information Management System of Beijing city are taken as samples. The ALGCNN model is used to classify and compare related data. It is found that in the MR, STT, and MPQA data sets, the classification accuracy and loss value of the ALGCNN model are better than other algorithms. HL is the lowest, the Pre is second only to the BERT algorithm, and the Re and F1 are both higher than other algorithms. From 2015 to 2019, the number of migrant children in different grades of elementary school shows a gradual increase. Among migrant children, the number of migrant children from other counties in this province is evidently higher than the number of migrant children from other provinces. Among children of migrant workers, the number of immigrants from other counties in this province is also notably higher than the number of immigrants from other provinces. With the gradual increase in the years, the proportion of township-level expenses shows a gradual decrease, whereas the proportion of district and county-level expenses shows a gradual increase. Moreover, the accuracy of the ALGCNN model in migrant children and local children data classification is 98.6 and 98.9%, respectively. The proportion of migrant children in the first and second grades of a primary school in Beijing city is obviously higher than that of local children. The average final score of local children was greatly higher than that of migrant children, whereas the scores of migrant children’s listening methods, learning skills, and learning environment adaptability are lower, which shows that an effective text classification model is established based on the CNN algorithm. In short, the children’s education costs, listening methods, learning skills, and learning environment adaptability are the main factors affecting migrant children’s educational integration, and this work provides a reference for the analysis of migrant children’s educational integration. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with (...) 97.50 % accuracy. The factor of DriveTrain has the most influence on MPG_City evaluation. Similar studies can be carried out for the evaluation of other characteristics of cars. (shrink)

One method for uncovering the subprocesses of mental processes is the “Additive Factors Method” (AFM). The AFM uses reaction time data from factorial experiments to infer the presence of separate processing stages. This paper investigates the conceptual status of the AFM. It argues that one of the AFM’s underlying assumptions is problematic in light of recent developments in cognitive neuroscience. Discussion begins by laying out the basic logic of the AFM, followed by an analysis of the challenge presented by (...) neural reuse. Following this, implications are analysed and avenues of response considered. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method (...) is suitable for the evaluation of car acceptability forecasting, can also be extended to all other areas. (shrink)

Recent work combining cognitive neuroscience with computational modelling suggests that distributed patterns of neural firing may represent probability distributions. This paper asks: what makes it the case that distributed patterns of firing, as well as carrying information about (correlating with) probability distributions over worldly parameters, represent such distributions? In examples of probabilistic population coding, it is the way information is used in downstream processing so as to lead to successful behaviour. In these cases content depends on factors beyond (...) bare information, contra Brian Skyrms’ (2010) view that representational content can be fully characterised in information-theoretic terms. (shrink)

Over 100 million Americans suffer from chronic pain, which causes more disability than any other medical condition in the United States at a cost of $560–$635 billion per year. Opioid analgesics are frequently used to treat CP. However, long term use of opioids can cause brain changes such as opioid-induced hyperalgesia that, over time, increase pain sensation. Also, opioids fail to treat complex psychological factors that worsen pain-related disability, including beliefs about and emotional responses to pain. Cognitive behavioral therapy (...) can be efficacious for CP. However, CBT generally does not focus on important factors needed for long-term functional improvement, including attainment of personal goals and the psychological flexibility to choose responses to pain. Acceptance and Commitment Therapy has been recognized as an effective, non-pharmacologic treatment for a variety of CP conditions. However, little is known about the neurologic mechanisms underlying ACT. We conducted an ACT intervention in women with chronic musculoskeletal pain. Functional magnetic resonance imaging data were collected pre- and post-ACT, and changes in functional connectivity were measured using Network-Based Statistics. Behavioral outcomes were measured using validated assessments such as the Acceptance and Action Questionnaire, the Chronic Pain Acceptance Questionnaire, the Center for Epidemiologic Studies Depression Scale, and the NIH Toolbox Neuro-QoLTM scales. Results suggest that, following the 4-week ACT intervention, participants exhibited reductions in brain activation within and between key networks including self-reflection, emotion, and cognitive control. These changes in connectivity strength were correlated with changes in behavioral outcomes including decreased depression and pain interference, and increased participation in social roles. This study is one of the first to demonstrate that improved function across the DMN, SN, and FPN may drive the positive outcomes associated with ACT. This study contributes to the emerging evidence supporting the use of neurophysiological indices to characterize treatment effects of alternative and complementary mind-body therapies. (shrink)

Is the basis of criminality an act that causes harm, or an act undertaken with the belief that one will cause harm? The present study takes a cognitive neuroscience approach to investigating how information about an agent’s beliefs and an action’s conse- quences contribute to moral judgment. We build on prior devel- opmental evidence showing that these factors contribute differ- entially to the young child’s moral judgments coupled with neurobiological evidence suggesting a role for the right tem- poroparietal junction (...) (RTPJ) in belief attribution. Participants read vignettes in a 2 2 design: protagonists produced either a negative or neutral outcome based on the belief that they were causing the negative outcome (‘‘negative’’ belief) or the neutral outcome (‘‘neutral’’ belief). The RTPJ showed significant activation above baseline for all four conditions but was modulated by an interaction between belief and outcome. Specifically, the RTPJ response was highest for cases of attempted harm, where protag- onists were condemned for actions that they believed would cause harm to others, even though the harm did not occur. The results not only suggest a general role for belief attribution during moral judgment, but also add detail to our understanding of the inter- action between these processes at both the neural and behavioral levels. (shrink)

In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the (...) above-mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real-time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural network-based vehicle classification system to improve robustness of vehicle classification in real-time applications. We present a vehicle dataset comprising of 10,000 images categorized into six-common vehicle classes considering adverse illuminous conditions to achieve robustness in real-time vehicle classification systems. Initially, pretrained AlexNet, GoogleNet, Inception-v3, VGG, and ResNet are fine-tuned on self-constructed vehicle dataset to evaluate their performance in terms of accuracy and convergence. Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine-tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. Finally, a comparison study has been carried out between the proposed and existing vehicle classification methods to evaluate the effectiveness of the proposed vehicle classification system. Consequently, our proposed system achieved 99.68%, 99.65%, and 99.56% accuracy, precision, and F1-score on our self-constructed dataset. (shrink)

To what extent is the neural organization of language dependent on factors specific to the modalities in which language is perceived and through which it is produced? That is, is the left-hemisphere dominance for language a function of a linguistic specialization or a function of some domain-general specialization(s), such as temporal processing or motor planning? Investigations of the neurobiology of signed language can help answer these questions. As with spoken languages, signed languages of the deaf display complex grammatical (...) structure but are perceived and produced via radically different modalities. Thus, by mapping out the neurological similarities and differences between signed and spoken language, it is possible to identify modality-specific contributions to brain organization for language. Research to date has shown a significant degree of similarity in the neurobiology of signed and spoken languages, suggesting that the neural organization of language is largely modality-independent. (shrink)

Violent extremism is often explicitly motivated by commitment to abstract ideals such as the nation or divine law – so-called “sacred” values that are relatively insensitive to material incentives and define our primary reference groups. Moreover, extreme pro-group behavior seems to intensify after social exclusion. This fMRI study explores underlying neural and behavioral relationships between sacred values, violent extremism, and social exclusion. Ethnographic fieldwork and psychological surveys were carried out among young men from a European Muslim community in neighborhoods (...) in and around Barcelona, Spain. Candidates for an fMRI experiment were selected from those who expressed willingness to engage in or facilitate, violence associated with jihadist causes. In the scanner, participants were assessed for their willingness to fight and die for in-group sacred values before and after an experimental manipulation using Cyberball, a toss ball game known to yield strong feelings of social exclusion. Results indicate that neural activity associated with sacred value processing in a sample vulnerable to recruitment into violent extremism shows marked activity in the left inferior frontal gyrus, a region previously associated with sacred values and rule retrieval. Participants also behaviorally expressed greater willingness to fight and die for sacred versus non-sacred values, consistent with previous studies of combatants and noncombatants. The social exclusion manipulation specifically affected non-sacred values, increasing their similarities with sacred values in terms of heightened left inferior frontal activity and greater expressed willingness to fight and die. These findings suggest that sacralization of values interacts with willingness to engage in extreme behavior in populations vulnerable to radicalization. In addition, social exclusion may be a relevant factor motivating violent extremism and consolidation of sacred values. If so, counteracting social exclusion and sacralization of values should figure into policies to prevent radicalization. (shrink)

Moral judgment depends critically on theory of mind, reasoning about mental states such as beliefs and intentions. People assign blame for failed attempts to harm and offer forgiveness in the case of accidents. Here we use fMRI to investigate the role of ToM in moral judgment of harmful vs. helpful actions. Is ToM deployed differently for judgments of blame vs. praise? Participants evaluated agents who produced a harmful, helpful, or neutral outcome, based on a harmful, helpful, or neutral intention; participants (...) made blame and praise judgments. In the right temporo-parietal junction, and, to a lesser extent, the left TPJ and medial prefrontal cortex, the neural response reflected an interaction between belief and outcome factors, for both blame and praise judgments: The response in these regions was highest when participants delivered a negative moral judgment, i.e., assigned blame or withheld praise, based solely on the agent's intent. These results show enhanced attention to mental states for negative moral verdicts based exclusively on mental state information. (shrink)

Dysfunction or injury of pain-transmitting primary afferents' central pathways can result in pain. The organism as a whole responds to such injury and consequently many symptoms of neuropathic pain develop. The nervous system responds to painful events and injury with neuroplasticity. Both peripheral sensitization and central sensitization take place and are mediated by a number of biochemical factors, including genes and receptors. Correction of altered receptors activity is the logical way to intervene therapeutically. [berkley; blumberg et al.; coderre & (...) katz; dickenson; mcmahon; wiesenfeld-hallin et al.]. (shrink)

In previous research Libet discovered that a critical time period for neural activation is necessary in order for a stimulus to become conscious. This necessary time period varies from subject to subject. In this current study, six subjects for whom the time for neural activation of consciousness had been previously determined were administered a battery of psychological tests on the basis of which ratings were made of degree of repressiveness. As hypothesized, repressive subjects had a longer critical time (...) period for neural activation of consciousness, suggesting the possibility that this neurophysiological time factor is a necessary condition for the development of repression. (shrink)

Over the past two decades, research on neural transplantation in animal models of neurodegeneration has provided provocative in sights into the therapeutic use of grafted tissue for various neurological diseases. Although great strides have been made and functional benefits gained in these animal models, much information is still needed with regard to transplantation in human patients. Several factors are unique to human disease, for example, age of the recipient, duration of disease, and drug interaction with grafted cells; these (...) need to be explored before grafting can be considered a safe and effective therapeutic tool. (shrink)

In previous research Libet discovered that a critical time period for neural activation is necessary in order for a stimulus to become conscious. This necessary time period varies from subject to subject. In this current study, six subjects for whom the time for neural activation of consciousness had been previously determined were administered a battery of psychological tests on the basis of which ratings were made of degree of repressiveness. As hypothesized, repressive subjects had a longer critical time (...) period for neural activation of consciousness, suggesting the possibility that this neurophysiological time factor is a necessary condition for the development of repression. (shrink)

This article describes key paradigms employed to assess deception and reviews the main neuroscience-based technologies that have been employed to investigate the neural correlates of deception: electroencephalography, functional magnetic resonance imaging, and transcranial direct current stimulation. Any potential use of neuroscience-based methods to detect deception in real-life situations requires successful classification in single subjects. It describes findings on the single subject performance of these methods and addresses the effects of two factors that are problematic for all deception detection (...) methods, the potential use of countermeasures, strategies used by subjects to defeat the deception detection tests, and the potential role of false memories and of incidental encoding. It briefly outlines some of the ethical issues associated with these technologies to detect deceptive behavior. (shrink)

Plamondon & Alimi (P&A) have unified much data on speed/accuracy trade-offs during reaching movements using a delta-lognormal form factor that describes notably neuromuscular systems. Their approach raises questions about whether a large number of systems is needed, whether they are linear, and whether the results disclose the neural design principles that control reaching behaviors. The authors admit that (sect. 6, para. 4).

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on (...) a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%. (shrink)

In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in (...) hospitals. The evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy. (shrink)

Many studies have observed modulation of the amplitude of the neural index mismatch negativity (MMN) related to which member of a phoneme contrast [phoneme A, phoneme B] serves as the frequent (standard) and which serves as the infrequent (deviant) stimulus (i.e., AAAB vs. BBBA) in an oddball paradigm. Explanations for this amplitude modulation range from acoustic to linguistic factors. We tested whether exchanging the role of the mid vowel /ε/ vs. high vowel /ɪ/ of English modulated MMN amplitude (...) and whether the pattern of modulation was compatible with an underspecification account, in which the underspecified height values are [−high] and [−low]. MMN was larger for /ε/ as the deviant, but only when compared across conditions to itself as the standard. For the within-condition comparison, MMN was larger to /ɪ/ deviant minus /ε/ standard than to the reverse. A condition order effect was also observed. MMN amplitude was smaller to the deviant stimulus if it had previously served as the standard. In addition, the amplitudes of late discriminative negativity (LDN) showed similar asymmetry. LDN was larger for deviant /ε/ than deviant /ɪ/ when compared to themselves as the standard. These findings were compatible with an underspecification account, but also with other accounts, such as the Natural Referent Vowel model and a prototype model; we also suggest that non-linguistic factors need to be carefully considered as additional sources of speech processing asymmetries. (shrink)

The literature on common pool resource (CPR) governance lists numerous factors that influence whether a given CPR system achieves ecological long-term sustainability. Up to now there is no comprehensive model to integrate these factors or to explain success within or across cases and sectors. Difficulties include the absence of large-N-studies (Poteete 2008), the incomparability of single case studies, and the interdependence of factors (Agrawal and Chhatre 2006). We propose (1) a synthesis of 24 success factors based (...) on the current SES framework and a literature review; (2) the application of neural networks on a database of CPR management case studies in an attempt to test the viability of this synthesis. This method allows us to obtain an implicit quantitative and rather precise model of the interdependencies in CPR systems. Given such a model, every success factor in each case can be manipulated separately, yielding different predictions for success. This could become be a fast and inexpensive way to analyze, predict and optimize performance for communities world-wide facing CPR challenges. Existing theoretical frameworks could be improved as well. (shrink)

This is the first book to deal with both the psychological and neurobiological mechanisms in appetites for drugs, food, sex, and gambling, and considers whether there are common factors between them. The authors approach this by looking at the bases of both normal and abnormal appetites in humans. The focus on human appetites will be of great interest to psychologists and clinicians alike.The EBBS Publications Series is designed to provide researchers and students with authoritative, topical reviews of major areas (...) in the brain and behaviour sciences. Each volume will include specially commissioned and edited chapters by leading researchers, presented in a lively and accessible style. (shrink)

The sophisticated function of the central nervous system (CNS) is largely supported by proper interactions between neural cells and blood vessels. Accumulating evidence has demonstrated that neurons and glial cells support the formation of blood vessels, which in turn, act as migratory scaffolds for these cell types. Neural progenitors are also involved in the regulation of blood vessel formation. This mutual interaction between neural cells and blood vessels is elegantly controlled by several chemokines, growth factors, extracellular (...) matrix, and adhesion molecules such as integrins. Recent research has revealed that newly migrating cell types along blood vessels repel other preexisting migrating cell types, causing them to detach from the blood vessels. In this review, we discuss vascular formation and cell migration, particularly during development. Moreover, we discuss how the crosstalk between blood vessels and neurons and glial cells could be related to neurodevelopmental disorders. (shrink)

Infertility is recognized as a major problem of modern society. Assisted Reproductive Technology is the one of many available treatment options to cure infertility. However, the efficiency of the ART treatment is still inadequate. Therefore, the procedure’s quality is constantly improving and there is a need to determine statistical predictors as well as contributing factors to the successful treatment. There is a concern over the application of adequate statistical analysis to clinical data: should classic statistical methods be used or (...) would it be more appropriate to apply advanced data mining technologies? By comparing two statistical models, Multivariable Logistic Regression analysis and Artificial Neural Network it has been demonstrated that Multivariable Logistic Regression analysis is more suitable for theoretical interest but the Artificial Neural Network method is more useful in clinical prediction. (shrink)

Neural Stem Cells (NSC) are present in the developing and adult CNS. In both the embryonic and adult neurogenic regions, β1 integrins may act as sensors for the changing extracellular matrix. Here we highlight the integrative functions that β1 integrins may play in the “niche” by regulating NSC growth factor responsiveness in a timely and spatially controlled manner. β1 integrins may provide NSC with the capacity to react to a dynamic “niche”, and to respond adequately by either remaining as (...) stem cells or by differentiating and migrating away to shape the developing cortex. © 2005 Wiley Periodicals, Inc. BioEssays 27:698–707, 2005. © 2005 Wiley Periodicals, Inc. (shrink)

There is increasing interest in how the pupil dynamics of the eye reflect underlying cognitive processes and brain states. Problematic, however, is that pupil changes can be due to non-cognitive factors, for example luminance changes in the environment, accommodation and movement. In this paper we consider how by modeling the response of the pupil in real-world environments we can capture the non-cognitive related changes and remove these to extract a residual signal which is a better index of cognition and (...) performance. Specifically, we utilize sequence measures such as fixation position, duration, saccades, and blink-related information as inputs to a deep recurrent neural network (RNN) model for predicting subsequent pupil diameter. We build and evaluate the model for a task where subjects are watching educational videos and subsequently asked questions based on the content. Compared to commonly-used models for this task, the RNN had the lowest errors rates in predicting subsequent pupil dilation given sequence data. Most importantly was how the model output related to subjects' cognitive performance as assessed by a post-viewing test. Consistent with our hypothesis that the model captures non-cognitive pupil dynamics, we found (1) the model's root-mean square error was less for lower performing subjects than for those having better performance on the post-viewing test, (2) the residuals of the RNN (LSTM) model had the highest correlation with subject post-viewing test scores and (3) the residuals had the highest discriminability (assessed via area under the ROC curve, AUC) for classifying high and low test performers, compared to the true pupil size or the RNN model predictions. This suggests that deep learning sequence models may be good for separating components of pupil responses that are linked to luminance and accommodation from those that are linked to cognition and arousal. (shrink)

Our model integrates the nativist assumption of prespecified neural structures underpinning basic emotions with the constructionist view that emotions are assembled from psychological constituents. From a dynamic systems perspective, the nervous system self-organizes in different ways at different time scales, in relation to functions served by emotions. At the evolutionary scale, brain parts and their connections are specified by selective pressures. At the scale of development, connectivity is revised through synaptic shaping. At the scale of real time, temporary networks (...) of synchronized activity mediate responses to situations. To the degree that humans share common emotional functions, neural structuration is similar across scales, giving rise to “basic” emotions. However, unique developmental and situational factors select for neural configurations mediating emotional variants. (shrink)

In this Letter to the Editor, we seize the opportunity to respond to the recent comments by Anzulewicz and Wierzchoń, and further clarify and extend the scope of our original paper. We re‐emphasize that conscious experiences come in degrees, and that there are several factors that determine this degree. Endorsing the suggestions of Anzulewicz and Wierzchoń, we discuss that besides low‐level attentional mechanisms, high‐level attentional and non‐attentional mechanisms might also modulate the quality of conscious experiences.

The automated valuation of benchmark land price plays an essential role in regulating land demand in Chinese real-estate market as the big data are currently accumulated rapidly. However, this problem becomes highly challenging due to the multidimension, large volume, and nonlinearity of the land price-influencing factors. In this paper, an effective data-driven automated valuation framework is proposed for valuing real estate assets by combining a GIS and neural network technologies. This framework can automatically obtain the values of spatial (...) factors affecting land price from GIS and generate training set data for training the neural network to identify the complex relationship between all kinds of factors and benchmark land prices. The effectiveness and universality of the framework is verified via the data of benchmark land prices in Wuhan. The framework can be applied for automated benchmark land price valuation in other cities. (shrink)

The aim of this paper is twofold: (1) to assess whether the construct of neural representations plays an explanatory role under the variational free-energy principle and its corollary process theory, active inference; and (2) if so, to assess which philosophical stance - in relation to the ontological and epistemological status of representations - is most appropriate. We focus on non-realist (deflationary and fictionalist-instrumentalist) approaches. We consider a deflationary account of mental representation, according to which the explanatorily relevant contents of (...) neural representations are mathematical, rather than cognitive, and a fictionalist or instrumentalist account, according to which representations are scientifically useful fictions that serve explanatory (and other) aims. After reviewing the free-energy principle and active inference, we argue that the model of adaptive phenotypes under the free-energy principle can be used to furnish a formal semantics, enabling us to assign semantic content to specific phenotypic states (the internal states of a Markovian system that exists far from equilibrium). We propose a modified fictionalist account: an organism-centered fictionalism or instrumentalism. We argue that, under the free-energy principle, pursuing even a deflationary account of the content of neural representations licenses the appeal to the kind of semantic content involved in the aboutness or intentionality of cognitive systems; our position is thus coherent with, but rests on distinct assumptions from, the realist position. We argue that the free-energy principle thereby explains the aboutness or intentionality in living systems and hence their capacity to parse their sensory stream using an ontology or set of semantic factors. (shrink)

After people learn to sort objects into categories they see them differently. Members of the same category look more alike and members of different categories look more different. This phenomenon of within-category compression and between-category separation in similarity space is called categorical perception (CP). It is exhibited by human subjects, animals and neural net models. In backpropagation nets trained first to auto-associate 12 stimuli varying along a onedimensional continuum and then to sort them into 3 categories, CP arises as (...) a natural side-effect because of four factors: (1) Maximal interstimulus separation in hidden-unit space during autoassociation learning, (2) movement toward linear separability during categorization learning, (3) inverse-distance repulsive force exerted by the between-category boundary, and (4) the modulating effects of input iconicity, especially in interpolating CP to untrained regions of the continuum. Once similarity space has been "warped" in this way, the compressed and separated "chunks" have symbolic labels which could then be combined into symbol strings that constitute propositions about objects. The meanings of such symbolic representations would be "grounded" in the system's capacity to pick out from their sensory projections the object categories that the propositions were about. (shrink)

We contend that if efficiency and reliability are important factors in neural information processing then distributed, not localist, representations are “evolution's best bet.” We note that distributed codes are the most efficient method for representing information, and that this efficiency minimizes metabolic costs, providing adaptive advantage to an organism.

By age 85, most adults manifest some degree of motor impairment. However, in most individuals a specific etiology for motor decline and treatment to modify its inexorable progression cannot be identified. Recent clinical-pathologic studies provide evidence that mixed-brain pathologies are commonly associated with late-life motor impairment. Yet, while nearly all older adults show some degree of accumulation of Alzheimer’s disease and related dementias pathologies, the extent to which these pathologies contribute to motor decline varies widely from person to person. Slower (...) or faster than expected motor decline in the presence of brain injury and/or pathology has been conceptualized as more or less “resilience” relative to the average person This suggests that other factors, such as lifestyles or other neurobiologic indices may offset or exacerbate the negative effects of pathologies via other molecular pathways. The mechanisms underlying neural motor resilience are just beginning to be illuminated. Unlike its cousin, cognitive resilience which is restricted to neural mechanisms above the neck, the motor system extends the total length of the CNS and beyond the CNS to reach muscle and musculoskeletal structures, all of which are crucial for motor function. Building on prior work, we propose that by isolating motor decline unrelated to neuropathologies and degeneration, investigators can identify genes and proteins that may provide neural motor resilience. Elucidating these molecular mechanisms will advance our understanding of the heterogeneity of late-life motor impairment. This approach will also provide high value therapeutic targets for drug discovery of therapies that may offset the negative motor consequences of CNS pathologies that are currently untreatable. (shrink)

Behaviorally assessing residual consciousness of acutely comatose patients involves a high rate of false-negatives. That is, long-term behavioral assessment shows that 41% of vegetative state patients in fact have residual consciousness. Nonetheless, surrogates need to remove ventilation before the acute-phase passes away if they want to induce medico-legal death due to pragmatic factors, such as financial costs. So, surrogate decision-making regarding behaviorally nonresponsive acutely comatose patients involves a moral dilemma: should we ignore the chance that patients have residual consciousness (...) for the sake of pragmatic factors? This paper examines whether neural methods can resolve the moral dilemma. Neural methods are used to assess residual consciousness of behaviorally nonresponsive postcomatose patients. For instance, by instructing a vegetative state patient to imagine wiggling all of her toes, conscious- ness is ascribed if brain activities are localized in the supplementary motor area. Since the most extensive application of neural methods has been in chronic population, it is unclear whether such methods can resolve the moral dilemma. I argue that neural methods also involve a high rate of false-negatives because current tasks of neural methods are structurally misguided. Given the argument, there is no significant difference between behavioral and neural methods regarding the moral dilemma. (shrink)