Routine action sequences critically rely on neural mechanisms maintaining contextual and temporal information to disambiguate similar tasks (e.g. making coffee or tea). In this study we show the involvement of areas in temporal and lateral prefrontal cortices in maintaining temporal and contextual information for the execution of hierarchically‐organized action sequences.

In cognitive neuroscience, dissociating the brain networks that ing—has thus become one of the best empirical situations subtend conscious and nonconscious memories constitutes a through which to study the mechanisms of implicit learning, very complex issue, both conceptually and methodologically.

Nissen and Bullemer's serial reaction time task has proven to be a useful model task for exploring implicit sequence learning. Neuropsychological research indicates that SRT learning may depend on the integrity of the basal ganglia, but not on medial temporal and diencephalic structures that are crucial for explicit learning. Recent neuroimaging research demonstrates that motor cortical areas , prefrontal, and parietal cortex also may be involved. This paper reviews this neuropsychological and neuroimaging research, but finds it lacking specific links between (...) structure and function. In order to promote better functional hypotheses, the second part of the paper examines the function of these brain areas from a broader perspective. Neuroimaging and neuropsychological research with human subjects, as well as neurophysiological and lesion research with animals, suggests a number component operations that these brain mechanisms may contribute to learning in the SRT task. (shrink)

Spoken language consists of a linear sequence of units, from which the existence of particular underlying hierarchical processing mechanisms is inferred. Uddén et al. use graph theory to provide a framework for describing the possible structural relationships that may underlie a linear output sequence. Being more explicit in defining different structures can help identifying and testing for such structures in AGL experiments, as well as help showing how behavioral and neuroimaging data reveals signatures of hierarchical processing in humans.

Spoken language consists of a linear sequence of units, from which the existence of particular underlying hierarchical processing mechanisms is inferred. Uddén et al. use graph theory to provide a framework for describing the possible structural relationships that may underlie a linear output sequence. Being more explicit in defining different structures can help identifying and testing for such structures in AGL experiments, as well as help showing how behavioral and neuroimaging data reveals signatures of hierarchical processing in humans.

The detection and categorization of animate motions is a crucial task underlying social interaction and perceptual decision making. Neural representations of perceived animate objects are partially located in the primate cortical region STS, which is a region that receives convergent input from intermediate-level form and motion representations. Populations of STS cells exist which are selectively responsive to specific animated motion sequences, such as walkers. It is still unclear how and to what extent form and motion information contribute to (...) the generation of such representations and what kind of mechanisms are involved in the learning processes. The article develops a cortical model architecture for the unsupervised learning of animated motion sequence representations. We demonstrate how the model automatically selects significant motion patterns as well as meaningful static form prototypes characterized by a high degree of articulation. Such key poses are selectively reinforced during learning through a cross talk between the motion and form processing streams. Furthermore, we show how sequence-selective representations are learned in STS by fusing static form and motion input from the segregated bottom-up driving input streams. Cells in STS, in turn, feed their activities recurrently to their input sites along top-down signal pathways. We show how such learned feedback connections enable predictions about future input as anticipation generated by sequence-selective STS cells. Network simulations demonstrate the computational capacity of the proposed model by reproducing several experimental findings from neurosciences and by accounting for recent behavioral data. (shrink)

How does the human brain link relational concepts to perceptual experience? For example, a speaker may say “the cup to the left of the computer” to direct the listener's attention to one of two cups on a desk. We provide a neural dynamic account for both perceptual grounding, in which relational concepts enable the attentional selection of objects in the visual array, and for the generation of descriptions of the visual array using relational concepts. In the model, activation in (...) neural populations evolves dynamically under the influence of both inputs and strong interaction as formalized in dynamic field theory. Relational concepts are modeled as patterns of connectivity to perceptual representations. These generalize across the visual array through active coordinate transforms that center the representation of target objects in potential reference objects. How the model perceptually grounds or generates relational descriptions is probed in 104 simulations that systematically vary the spatial and movement relations employed, the number of feature dimensions used, and the number of matching and nonmatching objects. We explain how sequences of decisions emerge from the time‐ and state‐continuous neural dynamics, how relational hypotheses are generated and either accepted or rejected, followed by the selection of new objects or the generation of new relational hypotheses. Its neural realism distinguishes the model from information processing accounts, its capacity to autonomously generate sequences of processing steps distinguishes it from deep neural network accounts. The model points toward a neural dynamic theory of higher cognition. (shrink)

In a Common Law system, legal practitioners need frequent access to prior case documents that discuss relevant legal issues. Case documents are generally very lengthy, containing complex sentence structures, and reading them fully is a strenuous task even for legal practitioners. Having a concise overview of these documents can relieve legal practitioners from the task of reading the complete case statements. Legal catchphrases are (multi-word) phrases that provide a concise overview of the contents of a case document, and automated generation (...) of catchphrases is a challenging problem in legal analytics. In this paper, we propose a novel supervised neural sequence tagging model for the extraction of catchphrases from legal case documents. Specifically, we show that incorporating document-specific information along with a sequence tagging model can enhance the performance of catchphrase extraction. We perform experiments over a set of Indian Supreme Court case documents, for which the gold-standard catchphrases (annotated by legal practitioners) are obtained from a popular legal information system. The performance of our proposed method is compared with that of several existing supervised and unsupervised methods, and our proposed method is empirically shown to be superior to all baselines. (shrink)

This paper proposes several recurrent neural network-based models for recognizing requisite and effectuation parts in Legal Texts. Firstly, we propose a modification of BiLSTM-CRF model that allows the use of external features to improve the performance of deep learning models in case large annotated corpora are not available. However, this model can only recognize RE parts which are not overlapped. Secondly, we propose two approaches for recognizing overlapping RE parts including the cascading approach which uses the sequence of BiLSTM-CRF (...) models and the unified model approach with the multilayer BiLSTM-CRF model and the multilayer BiLSTM-MLP-CRF model. Experimental results on two Japan law RRE datasets demonstrated advantages of our proposed models. For the Japanese National Pension Law dataset, our approaches obtained an \ score of 93.27% and exhibited a significant improvement compared to previous approaches. For the Japan Civil Code RRE dataset which is written in English, our approaches produced an \ score of 78.24% in recognizing RE parts that exhibited a significant improvement over strong baselines. In addition, using external features and in-domain pre-trained word embeddings also improved the performance of RRE systems. (shrink)

If artificial neural networks are ever to form the foundation for higher level cognitive behaviors in machines or to realize their full potential as explanatory devices for human cognition, they must show signs of autonomy, multifunction operation, and intersystem integration that are absent in most existing models. This model begins to address these issues by integrating predictive learning, sequence interleaving, and sequence creation components to simulate a spectrum of higher-order cognitive behaviors which have eluded the grasp of simpler systems. (...) Its capabilities are described based on simulations calling for increasing levels of functionality and are used to show how the model can progress from fundamental sequence learning and recall tasks to sophisticated behaviors such as an ability to solve simple mathematical expressions and a creative capacity for the formation and application of inductive rules. (shrink)

In sequence-space synaesthesia, members of linguistic sequences such as numbers, days of the week and letters of the alphabet are perceived to occupy spatial positions, either in the mind's eye or as locations in space around the body. In this chapter, we begin by considering the possible sequences that can induce this type of synaesthesia, with the focus on numbers, time-units and letters. We evaluate the various methods used to test the genuineness of self-reports of this type of (...) synaesthesia, and we make suggestions for novel techniques that could address the shortcomings of the current methods. Lastly, we briefly outline the current theories on the neural basis of this type of synaesthesia, including the recent reification hypothesis. We conclude by discussing how scientific findings with sequence-space synaesthetes can be beneficial for the study of cognitive psychology more generally. (shrink)

Throughout this research, imposing the training of an Artificial Neural Network (ANN) to play tic-tac-toe bored game, by training the ANN to play the tic-tac-toe logic using the set of mathematical combination of the sequences that could be played by the system and using both the Gradient Descent Algorithm explicitly and the Elimination theory rules implicitly. And so on the system should be able to produce imunate amalgamations to solve every state within the game course to make better (...) of results of winnings or getting draw. (shrink)

The dynamics of neural populations capture the laws of the mindThis paper focuses on the level of neural networks. Examining the case of recurrent neural networks, the paper argues that the dynamics of neural populations form a privileged level of explanation in cognitive science. According to Schöner, this level is privileged, because it enables cognitive scientists to discover the laws governing organisms’ cognition and behaviour.

According to the World Health Organisation, three to five million individuals are infected by influenza, and around 250,000 to 500,000 people die of this infectious disease worldwide. Influenza epidemics pose a serious public health threat. Moreover, graver dangers are encountered with influenza subtypes against which there is little or no preexisting human immunity. Such subtypes of influenza have the potential to cause devastating epidemics. Thus, enhancing surveillance systems for the purpose of detecting influenza epidemics in an early stage can quicken (...) response times and save millions of lives. This paper presents three adapting intelligence models: support vector machine regression, artificial neural network using particle swarm optimisation, and our intelligent time series to predict influenza epidemics. The novelty of the current study is that it proposes a new intelligent model to predict influenza outbreaks. The INTS model combines clustering with a time series model to enhance the prediction of influenza outbreaks. The innovation of our proposed model integrates the results obtained from the existing weighted exponential smoothing model with centroids obtained from clustering. We developed a surveillance system for influenza epidemics using Google search queries. The current research is based on a weighted version of the Center for Disease Control and Prevention influenza-like illness activity level obtained from the Center for Disease Control and Prevention data, as well as query data obtained from the Goggle search engine in the USA. The influenza-like illness data was collected from January 4, 2009, to December 27, 2015, stretching across a total time span of 312 weeks. Google Correlate was used to select search queries related to influenza epidemics. In total, 100 search queries were obtained from Google Correlate, 10 of which were better and more relevant search queries selected in this study. The model was evaluated using online Google search queries collected from Google Correlate. Standard measure performance MSE, RMSE, and MAE were employed to estimate the results of the proposed model. The empirical results of the INTS model showed MSE = 0.003, RMSE = 0.036, and MAE = 0.0185, indicating that the errors of the proposed model are very limited. A comparative model of predicting results between the INTS model, alternative Google Flu Trend, and autoregression with Google search data is also presented. The proposed model outperformed the existing models. (shrink)

Objective. Study of the validity and reliability of the discourse approach for the psycholinguistic understanding of the nature, structure, and features of the linguistic consciousness functioning. -/- Materials & Methods. This paper analyzes artificial neural network models built on the corpus of texts, which were obtained in the process of experimental research of the coronavirus quarantine concept as a new category of linguistic consciousness. The methodology of feedforward artificial neural networks (multilayer perceptron) was used in order to assess (...) the possibility of predicting the leading texts semantics based on the discourses ranks and their place in the respective linear sequence. Same baseline parameters were used to predict respondents' self-assessments of changes in their psychological well-being and in daily life routine during the quarantine, as well as to predict their preferences of the quarantine strategies. The study relied on basic ideas about discourse as a meaning constituted by the dispersion of other meanings (Foucault). The same dispersion mechanism realizes itself in interdiscourse interaction, forming a discursive formation at a higher level. The method of T-units (Hunt) was used to identify and count discourses in the texts. The ranking of discourses was provided based on the criterion of their semantic-syntactic autonomy. -/- Results. The conducted neural network modeling revealed a high accuracy in predicting the work of the linguistic consciousness functions associated with retrospective self-assessment and anticipatory imagination of the respondents. Another result of this modeling is a partial confirmation of the assumption concerning existence a relationship between the structural parameters of the discursive field (the rank of the discourses and their place in the respective linear sequence) and the leading semantics of the text. -/- Conclusions. A discourse approach to the study of linguistic consciousness, understanding of its structure and functioning features seems to be reasonably appropriate. The implementation of the approach presupposes the need to form a base of linguistic corpora with the inclusion in each text markup of such parameters as: the presence of specific discourses, their ranks, positions in the linear sequence of discourses. (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)

Long‐term modification of synaptic strength is thought to be the basic mechanism underlying the activity‐dependent refinement of neural circuits and the formation of memories engrammed on them. Studies ranging from cell culture preparations to humans subjects indicate that the decision of whether a synapse will undergo strengthening or weakening critically depends on the temporal order of presynaptic and postsynaptic activity. At many synapses, potentiation will be induced only when the presynaptic neuron fires an action potential within milliseconds before the (...) postsynaptic neuron fires, whereas weakening will occur when it is the postsynaptic neuron that fires first. Such processes might be important for the remodeling of neural circuits by activity during development and for network functions such as sequence learning and prediction. Ultimately, this synaptic property might also be fundamental for the cognitive process by which we structure our experience through cause and effect relations. BioEssays 24:212–222, 2002. © 2002 Wiley Periodicals, Inc.; DOI 10.1002/bies.10060. (shrink)

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine, a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and (...) can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models. We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. (shrink)

Day-to-day lives are affected globally by the epidemic coronavirus 2019. With an increasing number of positive cases, India has now become a highly affected country. Chronic diseases affect individuals with no time identification and impose a huge disease burden on society. In this article, an Efficient Recurrent Neural Network with Ensemble Classifier is built using VGG-16 and Alexnet with weighted model to predict disease and its level. The dataset is partitioned randomly into small subsets by utilizing mean-based splitting method. (...) Various models of classifier create a homogeneous ensemble by utilizing an accuracy-based weighted aging classifier ensemble, which is a weighted model’s modification. Two state of art methods such as Graph Sequence Recurrent Neural Network and Hybrid Rough-Block-Based Neural Network are used for comparison with respect to some parameters such as accuracy, precision, recall, f1-score, and relative absolute error. As a result, it is found that the proposed ERNN-EC method accomplishes accuracy of 95.2%, precision of 91%, recall of 85%, F1-score of 83.4%, and RAE of 41.6%. (shrink)

The number of comments/reviews for movies is enormous and cannot be processed manually. Therefore, machine learning techniques are used to efficiently process the user’s opinion. This research work proposes a deep neural network with seven layers for movie reviews’ sentiment analysis. The model consists of an input layer called the embedding layer, which represents the dataset as a sequence of numbers called vectors, and two consecutive layers of 1D-CNN for extracting features. A global max-pooling layer is used to reduce (...) dimensions. A dense layer for classification and a dropout layer are also used to reduce overfitting and improve generalization error in the neural network. A fully connected layer is the last layer to predict between two classes. Two movie review datasets are used and widely accepted by the research community. The first dataset contains 25,000 samples, half positive and half negative, whereas the second dataset contains 50,000 specimens of movie reviews. Our neural network model performs sentiment classification among positive and negative movie reviews called binary classification. The model achieves 92% accuracy on both datasets, which is more efficient than traditional machine learning models. (shrink)

A session-based recommendation system is designed to predict the user’s next click behavior based on an ongoing session. Existing session-based recommendation systems usually model a session into a sequence and extract sequence features through recurrent neural network. Although the performance is greatly improved, these procedures ignore the relationships between items that contain rich information. In order to obtain rich items embeddings, we propose a novel Recommendation Model based on Multi-channel Convolutional Neural Network for session-based recommendation, RMMCNN for brevity. (...) Specifically, we capture items' internal features from three dimensions through multi-channel convolutional neural network firstly. Next, we merge the internal features with external features obtained by a GRU unit. Then, both internal features and external features are merged by an attention mechanism together as the input of the transformation function. Finally, the probability distribution is taken as the output after the softmax function. Experiments on various datasets show that our method's precision and recommendation performance are better than those of other state-of-the-art approaches. (shrink)

Music can be described as sequences of events that are structured in pitch and time. Studying music processing provides insight into how complex event sequences are learned, perceived, and represented by the brain. Given the temporal nature of sound, expectations, structural integration, and cognitive sequencing are central in music perception (i.e., which sounds are most likely to come next and at what moment should they occur?). This paper focuses on similarities in music and language cognition research, showing that (...) music cognition research provides insight into the understanding of not only music processing but also language processing and the processing of other structured stimuli. The hypothesis of shared resources between music and language processing and of domain-general dynamic attention has motivated the development of research to test music as a means to stimulate sensory, cognitive, and motor processes. (shrink)

This paper investigated how implicit and explicit knowledge is reflected in event-related potentials in sequence learning. ERPs were recorded during a serial reaction time task. The results showed that there were greater RT benefits for standard compared with deviant stimuli later than early on, indicating sequence learning. After training, more standard triplets were generated under inclusion than exclusion tests and more standard triplets under exclusion than chance level, indicating that participants acquired both explicit and implicit knowledge. However, deviant targets elicited (...) enhanced N2 and P3 components for targets with explicit knowledge but a larger N2 effect for targets with implicit knowledge, revealing that implicit knowledge expresses itself in relatively early components and explicit knowledge in additional P3 components. The results help resolve current debate about the neural substrates supporting implicit and explicit learning. (shrink)

In vowel discrimination, commonly found discrimination patterns are directional asymmetries where discrimination is faster if differing vowels are presented in a certain sequence compared to the reversed sequence. Different models of speech sound processing try to account for these asymmetries based on either phonetic or phonological properties. In this study, we tested and compared two of those often-discussed models, namely the Featurally Underspecified Lexicon model and the Natural Referent Vowel framework. While most studies presented isolated vowels, we investigated a large (...) stimulus set of German vowels in a more naturalistic setting within minimal pairs. We conducted an mismatch negativity study in a passive and a reaction time study in an active oddball paradigm. In both data sets, we found directional asymmetries that can be explained by either phonological or phonetic theories. While behaviorally, the vowel discrimination was based on phonological properties, both tested models failed to explain the found neural patterns comprehensively. Therefore, we additionally examined the influence of a variety of articulatory, acoustical, and lexical factors but also the influence of factors beyond the well-known in depth via multiple regression analyses. The analyses revealed that the perceptual factor of perceived loudness has a greater impact than considered in the literature and should be taken stronger into consideration when analyzing preattentive natural vowel processing. (shrink)

The finding of fractal scaling (FS) in behavioral sequences has raised a debate on whether FS is a pervasive property of the cognitive system or is the result of specific processes. Inferences about the origins of properties in time sequences are causal. That is, as opposed to correlational inferences reflecting instantaneous symmetrical relations, causal inferences concern asymmetric relations lagged in time. Here, I integrate Granger‐causality with inferences about FS. Four simulations illustrate that causal analyses can isolate distinct FS (...) sources, whereas correlational techniques cannot. I then analyze three simultaneous sequences of responses from a database of word‐naming trials. I find that two, or perhaps three, distinct sources account for the presence of FS in these sequences, but FS is not a general property of the system. This suggests that FS arises due to the properties of a limited number of identifiable psychological and/or neural processes. Finally, I reanalyze a previously published dataset of acoustic frequency spectra using the new tools. The causality/criticality combination introduced here offers a new important perspective in the study of cognition. (shrink)

The finding of fractal scaling (FS) in behavioral sequences has raised a debate on whether FS is a pervasive property of the cognitive system or is the result of specific processes. Inferences about the origins of properties in time sequences are causal. That is, as opposed to correlational inferences reflecting instantaneous symmetrical relations, causal inferences concern asymmetric relations lagged in time. Here, I integrate Granger-causality with inferences about FS. Four simulations illustrate that causal analyses can isolate distinct FS (...) sources, whereas correlational techniques cannot. I then analyze three simultaneous sequences of responses from a database of word-naming trials. I find that two, or perhaps three, distinct sources account for the presence of FS in these sequences, but FS is not a general property of the system. This suggests that FS arises due to the properties of a limited number of identifiable psychological and/or neural processes. Finally, I reanalyze a previously published dataset of acoustic frequency spectra using the new tools. The causality/criticality combination introduced here offers a new important perspective in the study of cognition. (shrink)

The success of deep learning in natural language processing raises intriguing questions about the nature of linguistic meaning and ways in which it can be processed by natural and artificial systems. One such question has to do with subword segmentation algorithms widely employed in language modeling, machine translation, and other tasks since 2016. These algorithms often cut words into semantically opaque pieces, such as ‘period’, ‘on’, ‘t’, and ‘ist’ in ‘period|on|t|ist’. The system then represents the resulting segments in a dense (...) vector space, which is expected to model grammatical relations among them. This representation may in turn be used to map ‘period|on|t|ist’ (English) to ‘par|od|ont|iste’ (French). Thus, instead of being modeled at the lexical level, translation is reformulated more generally as the task of learning the best bilingual mapping between the sequences of subword segments of two languages; and sometimes even between pure character sequences: ‘p|e|r|i|o|d|o|n|t|i|s|t’ → ‘p|a|r|o|d|o|n|t|i|s|t|e’. Such subword segmentations and alignments are at work in highly efficient end-to-end machine translation systems, despite their allegedly opaque nature. The computational value of such processes is unquestionable. But do they have any linguistic or philosophical plausibility? I attempt to cast light on this question by reviewing the relevant details of the subword segmentation algorithms and by relating them to important philosophical and linguistic debates, in the spirit of making artificial intelligence more transparent and explainable. (shrink)

We propose that rapid eye movement (REM) and slow-wave sleep contribute differently to the formation of episodic memories. REM sleep is important for building up invariant object representations that eventually recur to gamma-band oscillations in the neocortex. In contrast, slow-wave sleep is more directly involved in the consolidation of episodic memories through replay of sequential neural activity in hippocampal place cells.

In a thought experiment we want to test how the emergence of adult neural stem cells could constitute an example for a scientific revolution in the sense of Thomas Kuhn. In his major work, The structure of scientific revolutions, 3rd edn, University of Chicago Press, Chicago (Kuhn 1996), the philosopher of science, Thomas Kuhn, states that scientific progress is not a cumulative process, but new theories appear by a rather revolutionary sequence of events. Kuhn built his theory on landmark (...) events taken from chemistry and physics, lacking examples from biology. Beginning with Ramon y Cajal’s famous quote, no new neurons after birth , from the early years of the twentieth century, and Reynolds and Weiss’s conflicting finding in 1992 of adult neural stem cells giving rise to new neurons, we will test how the finding of neural stem cells in the adult brain matches with Kuhn’s theory. The pivotal problem of defining a paradigm will be our main focus, since the emergence of adult neural stem cells has been acclaimed by the scientific community as the rebuttal of Ramon y Cajal’s paradigm. (shrink)

The Sparse, Distributed Memory (SDM) model (Kanerva, 1984) is compared to Hopfield-type, neural-network models. A mathematical framework for comparing the two models is developed, and the capacity of each model is investigated. The capacity of the SDM can be increased independent of the dimension of the stored vectors, whereas the Hopfield capacity is limited to a fraction of this dimension. The stored information is proportional to the number of connections, and it is shown that this proportionality constant is the (...) same for the SDM, the Hopfield model, and higher-order models. The models are also compared in their ability to store and recall temporal sequences of patterns. The SDM also includes time delays so that contextual information can be used to recover sequences. A generalization of the SDM allows storage of correlated patterns. (shrink)

The technology, which is implemented with cross platform tools, is proposed for modeling of gesture units of sign language, animation between states of gesture units with a combination of gestures. Implemented technology simulates sequence of gestures using virtual spatial hand model and performs recognition of dactyl items from camera input using trained on collected training dataset set convolutional neural network, based on the MobileNetv3 architecture, and with the optimal configuration of layers and network parameters. On the collected test dataset (...) accuracy of over 98% is achieved. (shrink)

Background: Although performance on the category fluency test is influenced by many cognitive functions, item-level scoring methods of CFT performance might be a promising way to capture aspects of semantic memory that are less influenced by intervenient abilities. One such approach is based on the calculation of correlation coefficients that quantify the association between item-level features and the serial order with which words are recalled.Methods: We explored the neural underpinnings of 10 of these correlational indices in a sample of (...) 40 healthy adults who completed a classic 1-min CFT and an MRI protocol inclusive of T1-weighted and resting-state fMRI sequences for the evaluation of the default-mode network. Two sets of linear models were defined to test the association between neural maps and each correlational index: a first set in which major demographic and clinical descriptors were controlled for and a second set in which, additionally, all other 9 correlational indices were regressed out.Results: In the analysis of the DMN, ‘SRO-frequency’, ‘SRO-dominance’ and ‘SRO-body-object interaction’ correlational indices were all negatively associated with the anterior portion of the right temporoparietal junction. The ‘SRO-frequency’ correlational index was also negatively associated with the right dorsal anterior cingulate and the ‘SRO-dominance’ correlational index with the right lateral prefrontal cortex. From the second set of models, the ‘SRO-typicality’ correlational index was positively associated with the left entorhinal cortex. No association was found in relation to grey matter maps.Conclusion: The ability to retrieve more difficult words during CFT performance as measured by the correlational indices between SRO and item-level descriptors is associated with DMN expression in regions deputed to attentional reorienting and processing of salience of infrequent stimuli and dominance status. Of all item-level features, typicality appears to be that most closely linked with entorhinal functioning and may thus play a relevant role in assessing its value in testing procedures for early detection of subtle cognitive difficulties in people with suspected Alzheimer’s degeneration. Although exploratory, these findings warrant further investigations in larger cohorts. (shrink)

The blood oxygen level dependent effect that provides the contrast in functional magnetic resonance imaging has been demonstrated to affect the linewidth of spectral peaks as measured with magnetic resonance spectroscopy and through this, may be used as an indirect measure of cerebral blood flow related to neural activity. By acquiring MR-spectra interleaved with frames without water suppression, it may be possible to image the BOLD effect and associated metabolic changes simultaneously through changes in the linewidth of the unsuppressed (...) water peak. The purpose of this study was to implement this approach with the MEGA-PRESS sequence, widely considered to be the standard sequence for quantitative measurement of GABA at field strengths of 3 T and lower, to observe how changes in both glutamate and GABA levels may relate to changes due to the BOLD effect. MR-spectra and fMRI were acquired from the occipital cortex of 20 healthy participants whilst undergoing intrascanner visual stimulation in the form of a red and black radial checkerboard, alternating at 8 Hz, in 90 s blocks comprising 30 s of visual stimulation followed by 60 s of rest. Results show very strong agreement between the changes in the linewidth of the unsuppressed water signal and the canonical haemodynamic response function as well as a strong, negative, but not statistically significant, correlation with the Glx signal as measured from the OFF spectra in MEGA-PRESS pairs. Findings from this experiment suggest that the unsuppressed water signal provides a reliable measure of the BOLD effect and that correlations with associated changes in GABA and Glx levels may also be measured. However, discrepancies between metabolite levels as measured from the difference and OFF spectra raise questions regarding the reliability of the respective methods. (shrink)

Synchronizing oscillations may be just one case of integration and/or coding, one which explains associations by concurrence. Understanding the sequencing of neural/behavioral events requires a clock mechanism that imposes structure behind mere associations, and may be best served by dissociating oscillations and synchronization in terms of physiologic and computational mechanisms.

Cervical spinal cord injuries often lead to loss of motor function in both hands and legs, limiting autonomy and quality of life. While it was shown that unilateral hand function can be restored after SCI using a hybrid electroencephalography/electrooculography brain/neural hand exoskeleton, it remained unclear whether such hybrid paradigm also could be used for operating two hand exoskeletons, e.g., in the context of bimanual tasks such as eating with fork and knife. To test whether EEG/EOG signals allow for fluent (...) and reliable as well as safe and user-friendly bilateral B/NHE control, eight healthy participants as well as four chronic tetraplegics performed a complex sequence of EEG-controlled bilateral grasping and EOG-controlled releasing motions of two exoskeletons visually presented on a screen. A novel EOG command performed by prolonged horizontal eye movements to the left or right was introduced as a reliable switch to activate either the left or right exoskeleton. Fluent EEG control was defined as average “time to initialize” grasping motions below 3 s. Reliable EEG control was assumed when classification accuracy exceeded 80%. Safety was defined as “time to stop” all unintended grasping motions within 2 s. After the experiment, tetraplegics were asked to rate the user-friendliness of bilateral B/NHE control using Likert scales. Average TTI and accuracy of EEG-controlled operations ranged at 2.14 ± 0.66 s and 85.89 ± 15.81% across healthy participants and at 1.90 ± 0.97 s and 81.25 ± 16.99% across tetraplegics. Except for one tetraplegic, all participants met the safety requirements. With 88 ± 11% of the maximum achievable score, tetraplegics rated the control paradigm as user-friendly and reliable. These results suggest that hybrid EEG/EOG B/NHE control of two assistive devices is feasible and safe, paving the way to test this paradigm in larger clinical trials performing bimanual tasks in everyday life environments. (shrink)

The early right anterior negativity (ERAN) in event-related potentials (ERPs) is typically elicited by syntactically unexpected events in Western tonal music. We examined how visual predictive information influences syntactic processing, how musical or non-musical cues have different effects, and how they interact with sequential effects between trials, which could modulate with the strength of the sense of established tonality. The EEG was recorded from musicians who listened to chord sequences paired with one of four types of visual stimuli; two (...) provided predictive information about the syntactic validity of the last chord through either musical notation of the whole sequence, or the word “regular” or “irregular”, while the other two, empty musical staves or a blank screen, provided no information. Half of the sequences ended with the syntactically invalid Neapolitan sixth chord, while the other half ended with the Tonic chord. Clear ERAN was observed in frontocentral electrodes in all conditions. A principal component analysis was performed on the grand average response in the audio-only condition, to separate spatio-temporal dynamics of different scalp areas as principal components and use them to extract auditory-related neural activities in the other visual-cue conditions. The first principal component (PC1) showed a symmetrical frontocentral topography, while the second (PC2) showed a right-lateralized frontal concentration. A source analysis confirmed the relative contribution of temporal sources to the former and a right frontal source to the latter. Cue predictability affected only the ERAN projected onto PC1, especially when the previous trial ended with the Tonic chord. The ERAN in PC2 was reduced in the trials following Neapolitan endings in general. However, the extent of this reduction differed between cue-types, whereby it was nearly absent when musical notation was used, regardless of whether the staves were filled with notes or empty. The results suggest that the right frontal areas carry out the primary role in musical syntactic analysis and integration of the ongoing context, which produce schematic expectations that, together with the veridical expectation incorporated by the temporal areas, inform musical syntactic processing in musicians. (shrink)

The accurate annotation of an unknown protein sequence depends on extant data of template sequences. This could be empirical or sets of reference sequences, and provides an exhaustive pool of probable functions. Individual methods of predicting dominant function possess shortcomings such as varying degrees of inter-sequence redundancy, arbitrary domain inclusion thresholds, heterogeneous parameterization protocols, and ill-conditioned input channels. Here, I present a rigorous theoretical derivation of various steps of a generic algorithm that integrates and utilizes several statistical methods (...) to predict the dominant function in unknown protein sequences. The accompanying mathematical proofs, interval definitions, analysis, and numerical computations presented are meant to offer insights not only into the specificity and accuracy of predictions, but also provide details of the operatic mechanisms involved in the integration and its ensuing rigor. The algorithm uses numerically modified raw hidden markov model scores of well defined sets of training sequences and clusters them on the basis of known function. The results are then fed into an artificial neural network, the predictions of which can be refined using the available data. This pipeline is trained recursively and can be used to discern the dominant principal function, and thereby, annotate an unknown protein sequence. Whilst, the approach is complex, the specificity of the final predictions can benefit laboratory workers design their experiments with greater confidence. -/- . (shrink)

This article proposes using structural description for graphical objects to solve an urgent task of trajectory analysis. A range of modern trajectory analysis approaches were analyzed and the best that is based on Graph Convolutional Neural Networks and Suffix Tree Clustering algorithm was chosen. Descripted ways to reduce computational sources for this neural network approach. This neural network was adapted to analyze structural description and advantages of this approach are shown.

American Academy of Pediatrics (AAP) and American College of Medical Genetics (ACMG) recently provided two recommendations about predictive genetic testing of children. The Clinical Sequencing Exploratory Research Consortium's Pediatrics Working Group compared these recommendations, focusing on operational and ethical issues specific to decision making for children. Content analysis of the statements addresses two issues: (1) how these recommendations characterize and analyze locus of decision making, as well as the risks and benefits of testing, and (2) whether the guidelines conflict or (...) come to different but compatible conclusions because they consider different testing scenarios. These statements differ in ethically significant ways. AAP/ACMG analyzes risks and benefits using best interests of the child and recommends that, absent ameliorative interventions available during childhood, clinicians should generally decline to order testing. Parents authorize focused tests. ACMG analyzes risks and benefits using the interests of the child and other family members and recommends that sequencing results be examined for additional variants that can lead to ameliorative interventions, regardless of age, which laboratories should report to clinicians who should contextualize the results. Parents must accept additional analysis. The ethical arguments in these statements appear to be in tension with each other. (shrink)

Colloquially, episodic memory is described as “the memory of personally experienced events”. Even though episodic memory has been studied in psychology and neuroscience for about six decades, there is still great uncertainty as to what episodic memory is. Here we ask how episodic memory should be characterized in order to be validated as a natural kind. We propose to conceive of episodic memory as a knowledge-like state that is identified with an experientially based mnemonic representation of an episode that allows (...) for a mnemonic simulation thereof. We call our analysis the Sequence Analysis of Episodic Memory since episodes will be analyzed in terms of sequences of events. Our philosophical analysis of episodic memory is driven and supported by experimental results from psychology and neuroscience. We discuss selected experimental results that provide exemplary evidence for uniform causal mechanisms underlying the properties of episodic memory and argue that episodic memory is a natural kind. The argumentation proceeds along three cornerstones: First, psychological evidence suggests that a violation of any of the proposed conditions for episodic memory amounts to a deficiency of episodic memory and no form of memory or cognitive process but episodic memory fulfills them. Second, empirical results support a claim that the principal anatomical substrate of episodic memory is the hippocampus. Finally, we can pin down causal mechanisms onto neural activities in the hippocampus to explain the psychological states and processes constituting episodic memory. (shrink)

In a recent paper, Cheng and Werning (2016) argue that the class of episodic memories constitutes a natural kind. Endorsing the homeostatic property cluster view of natural kinds, they suggest that episodic memories can be characterized by a cluster of properties unified by an underlying neural mechanism for coding sequences of events. Here, I argue that Cheng & Werning’s proposal faces some significant, and potentially insurmountable, difficulties. Two are described as most prominent. First, the proposal fails to satisfy (...) an important normative constraint on natural kind theorizing, not providing the requisite theoretical resources for arbitration between rival taxonomies of memory. Second, the proposal is in a direct tension with a foundational principle of the HPC view: the rejection of essentialism. This has far-reaching consequences, threatening to undermine the coherence of the proposal. (shrink)

The predictable rhythmic structure is important to most ecologically relevant sounds for humans, such as is found in the rhythm of speech or music. This study addressed the question of how rhythmic predictions are maintained in the auditory system when there are multiple perceptual interpretations occurring simultaneously and emanating from the same sound source. We recorded the electroencephalogram (EEG) while presenting participants with a tone sequence that had two different tone feature patterns, one based on the sequential rhythmic variation in (...) tone duration and the other on sequential rhythmic variation in tone intensity. Participants were presented with the same sound sequences and were instructed to listen for the intensity pattern (ignore fluctuations in duration) and press a response key to detected pattern deviants (attend intensity pattern task); to listen to the duration pattern (ignore fluctuations in intensity) and make a button press to duration pattern deviants (attend duration pattern task), and to watch a movie and ignore the sounds presented to their ears (attend visual task). Both intensity and duration patterns occurred predictably 85% of the time, thus the key question involved evaluating how the brain treated the irrelevant feature patterns (standards and deviants) while performing an auditory or visual task. We expected that task-based feature patterns would have a more robust brain response to attended standards and deviants than the unattended feature patterns. Instead, we found that the neural entrainment to the rhythm of the standard attended patterns had similar power to the standard of the unattended feature patterns. In addition, the infrequent pattern deviants elicited the event-related brain potential called the mismatch negativity component (MMN). The MMN elicited by task-based feature pattern deviants had a similar amplitude to MMNs elicited by unattended pattern deviants that were unattended because they were not the target pattern or because the participant ignored the sounds and watched a movie. Thus, these results demonstrate that the brain tracks multiple predictions about the complexities in sound streams and can automatically track and detect deviations with respect to these predictions. This capability would be useful for switching attention rapidly among multiple objects in a busy auditory scene. (shrink)