Transcranial Magnetic Stimulation to Study the Neural Network Account of Language

2019 ◽  
pp. 93-114
Author(s):  
Teresa Schuhmann
Keyword(s):  
Neural Network ◽  
Transcranial Magnetic Stimulation ◽  
Language Processing ◽  
Magnetic Stimulation ◽  
Network Activity ◽  
Temporal Characteristics ◽  
Language Studies ◽  
The Neural Network ◽  
Functional Relevance ◽  
The Many

This chapter illustrates how transcranial magnetic stimulation (TMS) can be used to investigate the functional relevance and temporal characteristics of language-related brain networks for various aspects of language processing. In contrast to neuroimaging methods establishing mainly correlative relationships between patterns of neural activity and cognitive functions, TMS enables a direct manipulation of neural network activity with respective functional consequences on behavior and cognition. Examples of whether and how TMS has been demonstrated to unravel such functional brain-behavior relationships in the domain of language processing, which is unarguably one of the most complex human abilities one could aim to investigate from a neuroscience perspective, are presented. This chapter therefore first introduces the basic principles and mechanisms of action underlying TMS, including the many possible application protocols. Based on the understanding that TMS can investigate both the spatial as well as temporal characteristics of the neural correlates of language, the suitability and limitations of TMS in language research are discussed. Next, examples of TMS language studies that have successfully employed the different advantages of TMS are presented. Finally, the applicability of TMS for clinical populations in the context of language-related deficits such as aphasia are reviewed briefly, followed by a short outlook on future perspectives of TMS in the study of language.

Tagging terms in text

Terminology ◽  
2022 ◽  
Author(s):  
Ayla Rigouts Terryn ◽  
Véronique Hoste ◽  
Els Lefever
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Language Processing ◽  
Conditional Random Fields ◽  
Traditional Approach ◽  
Learning Approaches ◽  
Term Extraction ◽  
The Neural Network ◽  
Predicted Probability ◽  
Automatic Term Extraction

Abstract As with many tasks in natural language processing, automatic term extraction (ATE) is increasingly approached as a machine learning problem. So far, most machine learning approaches to ATE broadly follow the traditional hybrid methodology, by first extracting a list of unique candidate terms, and classifying these candidates based on the predicted probability that they are valid terms. However, with the rise of neural networks and word embeddings, the next development in ATE might be towards sequential approaches, i.e., classifying each occurrence of each token within its original context. To test the validity of such approaches for ATE, two sequential methodologies were developed, evaluated, and compared: one feature-based conditional random fields classifier and one embedding-based recurrent neural network. An additional comparison was added with a machine learning interpretation of the traditional approach. All systems were trained and evaluated on identical data in multiple languages and domains to identify their respective strengths and weaknesses. The sequential methodologies were proven to be valid approaches to ATE, and the neural network even outperformed the more traditional approach. Interestingly, a combination of multiple approaches can outperform all of them separately, showing new ways to push the state-of-the-art in ATE.

scholarly journals Comparison of rule-based and neural network models for negation detection in radiology reports

2020 ◽  
pp. 1-22 ◽  
Author(s):  
D. Sykes ◽  
A. Grivas ◽  
C. Grover ◽  
R. Tobin ◽  
C. Sudlow ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Language Processing ◽  
Network Models ◽  
Neural Network Models ◽  
Test Set ◽  
Rule Based ◽  
Radiology Reports ◽  
The Neural Network ◽  
Negation Detection

Abstract Using natural language processing, it is possible to extract structured information from raw text in the electronic health record (EHR) at reasonably high accuracy. However, the accurate distinction between negated and non-negated mentions of clinical terms remains a challenge. EHR text includes cases where diseases are stated not to be present or only hypothesised, meaning a disease can be mentioned in a report when it is not being reported as present. This makes tasks such as document classification and summarisation more difficult. We have developed the rule-based EdIE-R-Neg, part of an existing text mining pipeline called EdIE-R (Edinburgh Information Extraction for Radiology reports), developed to process brain imaging reports, (https://www.ltg.ed.ac.uk/software/edie-r/) and two machine learning approaches; one using a bidirectional long short-term memory network and another using a feedforward neural network. These were developed on data from the Edinburgh Stroke Study (ESS) and tested on data from routine reports from NHS Tayside (Tayside). Both datasets consist of written reports from medical scans. These models are compared with two existing rule-based models: pyConText (Harkema et al. 2009. Journal of Biomedical Informatics42(5), 839–851), a python implementation of a generalisation of NegEx, and NegBio (Peng et al. 2017. NegBio: A high-performance tool for negation and uncertainty detection in radiology reports. arXiv e-prints, p. arXiv:1712.05898), which identifies negation scopes through patterns applied to a syntactic representation of the sentence. On both the test set of the dataset from which our models were developed, as well as the largely similar Tayside test set, the neural network models and our custom-built rule-based system outperformed the existing methods. EdIE-R-Neg scored highest on F1 score, particularly on the test set of the Tayside dataset, from which no development data were used in these experiments, showing the power of custom-built rule-based systems for negation detection on datasets of this size. The performance gap of the machine learning models to EdIE-R-Neg on the Tayside test set was reduced through adding development Tayside data into the ESS training set, demonstrating the adaptability of the neural network models.

Achievements analysis of mooc English course based on fuzzy statistics and neural network clustering

2020 ◽  
Vol 39 (4) ◽  
pp. 5559-5569
Author(s):  
Meichen Jin
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
Language Processing ◽  
Learning Ability ◽  
Network Clustering ◽  
College English ◽  
English Curriculum ◽  
The Neural Network ◽  
Training Stage ◽  
Fuzzy Statistics

At present, the field of natural language will also introduce in-depth learning, using the concept of word vector, so that the neural network can also complete the work in the field of statistics. It can be said that the neural network has begun to show its advantages in the field of natural language processing. In this paper, the author analyzes the multimedia English course based on fuzzy statistics and neural network clustering. Different factors were classified, and scores were classified according to the number of characteristics of different categories. It can be seen that with the popularization of the Internet, MOOC teaching meets the requirements of the current college English curriculum, is a breakthrough in the traditional teaching mode, improves students’ participation, and enables students to learn independently. It not only conforms to the characteristics of College students, but also improves their learning effect. In the automatic scoring stage, the quantitative text features are extracted by the feature extractor in the pre-processing stage, and then the weights of network connections obtained in the training stage are used to score the weights comprehensively. This model can better reflect students’ autonomous learning ability and language application ability.

scholarly journals Posterior superior temporal sulcus supports sequence-based language processing: Evidence from low-frequency repetitive transcranial magnetic stimulation

2020 ◽  
Author(s):  
Louise Kyriaki ◽  
Gabrielle Todd ◽  
Matthias Schlesewsky ◽  
Joseph Devlin ◽  
Ina Bornkessel-Schlesewsky
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Language Processing ◽  
Sentence Processing ◽  
Word Order ◽  
Magnetic Stimulation ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Low Frequency ◽  
Superior Temporal Sulcus ◽  
English Speakers ◽  
Posterior Superior Temporal Sulcus

Understanding the sequence (i.e. word order) of linguistic input plays an important role in sentence comprehension, particularly in languages such as English (Bornkessel-Schlesewsky et al., 2015). Neuroimaging and clinical research shows that left posterior superior temporal sulcus (pSTS) contributes towards sequence processing in both linguistic and non-linguistic contexts (Bornkessel et al., 2005; Wilson et al., 2010). To test the causal contribution of left pSTS for sequence-dependent sentence processing, we applied image-guided low-frequency repetitive transcranial magnetic stimulation (1 Hz for 15 minutes at 90% resting motor threshold) to this region in 23 healthy native English speakers. Participants undertook an auditory sentence processing task and were asked to identify the sentential actor or undergoer. Sentences were either semantically plausible or were rendered implausible by an animacy violation (e.g. “The student will write the answer” versus “The answer will write the student”). After sham-rTMS (control condition), participants predominantly selected the first noun as the actor and second noun as the undergoer, relying strongly on sequence cues (word order) for interpretation as expected in English speakers. By contrast, after real-rTMS, participants were more likely to use animacy as a cue to interpretation, with higher selections of the animate noun as the actor and inanimate noun as the undergoer regardless of word order. This effect also interacted with question focus and response time. These results indicate that sequence-based language processing is reduced after low-frequency rTMS to pSTS, suggesting a role for pSTS in processing sequential aspects of language such as word order.

A Network Model of Perceptual Suppression Induced by Transcranial Magnetic Stimulation

2004 ◽  
Vol 16 (2) ◽  
pp. 309-331 ◽  
Author(s):  
Yoichi Miyawaki ◽  
Masato Okada
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Network Model ◽  
Magnetic Stimulation ◽  
Experimental Studies ◽  
Afferent Input ◽  
Network Activity ◽  
Neural Population ◽  
High Temporal Resolution ◽  
Magnetic Pulse ◽  
Dynamical Interaction

We modeled the inhibitory effects of transcranial magnetic stimulation (TMS) on a neural population. TMS is a noninvasive technique, with high temporal resolution, that can stimulate the brain via a brief magnetic pulse from a coil placed on the scalp. Because of these advantages, TMS is extensively used as a powerful tool in experimental studies of motor, perception, and other functions in humans. However, the mechanisms by which TMS interferes with neural activities, especially in terms of theoretical aspects, are totally unknown. In this study, we focused on the temporal properties of TMS-induced perceptual suppression, and we computationally analyzed the response of a simple network model of a sensory feature detector system to a TMS-like perturbation. The perturbation caused the mean activity to transiently increase and then decrease for a long period, accompanied by a loss in the degree of activity localization. When the afferent input consisted of a dual phase, with a strong transient component and a weak sustained component, there was a critical latency period of the perturbation during which the network activity was completely suppressed and converged to the resting state. The range of the suppressive period increased with decreasing afferent input intensity and reached more than 10 times the time constant of the neuron. These results agree well with typical experimental data for occipital TMS and support the conclusion that dynamical interaction in a neural population plays an important role in TMS-induced perceptual suppression.

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