scholarly journals An oscillating computational model can track pseudo-rhythmic speech by using linguistic predictions

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Sanne ten Oever ◽  
Andrea E Martin
Keyword(s):  
Computational Model ◽  
Sensory Processing ◽  
Empirical Data ◽  
Temporal Dynamics ◽  
Language Models ◽  
Brain Oscillations ◽  
Neuronal Oscillations ◽  
Speech Input ◽  
Temporal Phase ◽  
Speech Time

Neuronal oscillations putatively track speech in order to optimize sensory processing. However, it is unclear how isochronous brain oscillations can track pseudo-rhythmic speech input. Here we propose that oscillations can track pseudo-rhythmic speech when considering that speech time is dependent on content-based predictions flowing from internal language models. We show that temporal dynamics of speech are dependent on the predictability of words in a sentence. A computational model including oscillations, feedback, and inhibition is able to track pseudo-rhythmic speech input. As the model processes, it generates temporal phase codes, which are a candidate mechanism for carrying information forward in time. The model is optimally sensitive to the natural temporal speech dynamics and can explain empirical data on temporal speech illusions. Our results suggest that speech tracking does not have to rely only on the acoustics but could also exploit ongoing interactions between oscillations and constraints flowing from internal language models.

scholarly journals Linguistic constraints modulate speech timing in an oscillating neural network

2020 ◽  
Author(s):  
Sanne Ten Oever ◽  
Andrea E. Martin
Keyword(s):  
Sensory Processing ◽  
Speech Processing ◽  
Language Models ◽  
Speech Input ◽  
Speech Timing ◽  
Temporal Phase ◽  
Word Onset ◽  
Knowledge Of Language ◽  
Linguistic Constraints ◽  
Onset Delays

AbstractNeuronal oscillations putatively track speech in order to optimize sensory processing. However, it is unclear how isochronous brain oscillations can track pseudo-rhythmic speech input. Here we investigate how top-down predictions flowing from internal language models interact with oscillations during speech processing. We show that word-to-word onset delays are shorter when words are spoken in predictable contexts. A computational model including oscillations, feedback, and inhibition is able to track the natural pseudo-rhythmic word-to-word onset differences. As the model processes, it generates temporal phase codes, which are a candidate mechanism for carrying information forward in time in the system. Intriguingly, the model’s response is more rhythmic for non-isochronous compared to isochronous speech when onset times are proportional to predictions from the internal model. These results show that oscillatory tracking of temporal speech dynamics relies not only on the input acoustics, but also on the linguistic constraints flowing from knowledge of language.

scholarly journals Interacting roles of lateral and medial Orbitofrontal cortex in decision-making and learning : A system-level computational model

2019 ◽  
Author(s):  
Bhargav Teja Nallapu ◽  
Frédéric Alexandre
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Computational Model ◽  
Orbitofrontal Cortex ◽  
Temporal Dynamics ◽  
System Level ◽  
Interacting Networks ◽  
Cortical Regions ◽  
Experimental Findings

AbstractIn the context of flexible and adaptive animal behavior, the orbitofrontal cortex (OFC) is found to be one of the crucial regions in the prefrontal cortex (PFC) influencing the downstream processes of decision-making and learning in the sub-cortical regions. Although OFC has been implicated to be important in a variety of related behavioral processes, the exact mechanisms are unclear, through which the OFC encodes or processes information related to decision-making and learning. Here, we propose a systems-level view of the OFC, positioning it at the nexus of sub-cortical systems and other prefrontal regions. Particularly we focus on one of the most recent implications of neuroscientific evidences regarding the OFC - possible functional dissociation between two of its sub-regions : lateral and medial. We present a system-level computational model of decision-making and learning involving the two sub-regions taking into account their individual roles as commonly implicated in neuroscientific studies. We emphasize on the role of the interactions between the sub-regions within the OFC as well as the role of other sub-cortical structures which form a network with them. We leverage well-known computational architecture of thalamo-cortical basal ganglia loops, accounting for recent experimental findings on monkeys with lateral and medial OFC lesions, performing a 3-arm bandit task. First we replicate the seemingly dissociate effects of lesions to lateral and medial OFC during decision-making as a function of value-difference of the presented options. Further we demonstrate and argue that such an effect is not necessarily due to the dissociate roles of both the subregions, but rather a result of complex temporal dynamics between the interacting networks in which they are involved.Author summaryWe first highlight the role of the Orbitofrontal Cortex (OFC) in value-based decision making and goal-directed behavior in primates. We establish the position of OFC at the intersection of cortical mechanisms and thalamo-basal ganglial circuits. In order to understand possible mechanisms through which the OFC exerts emotional control over behavior, among several other possibilities, we consider the case of dissociate roles of two of its topographical subregions - lateral and medial parts of OFC. We gather predominant roles of each of these sub-regions as suggested by numerous experimental evidences in the form of a system-level computational model that is based on existing neuronal architectures. We argue that besides possible dissociation, there could be possible interaction of these sub-regions within themselves and through other sub-cortical structures, in distinct mechanisms of choice and learning. The computational framework described accounts for experimental data and can be extended to more comprehensive detail of representations required to understand the processes of decision-making, learning and the role of OFC and subsequently the regions of prefrontal cortex in general.

scholarly journals COVID-19 In United States: Statistical Analysis and Inferences From the Empirical Data

2021 ◽  
Author(s):  
Nivedita Rethnakar
Keyword(s):  
United States ◽  
Statistical Analysis ◽  
Statistical Inference ◽  
Empirical Data ◽  
Respiratory Diseases ◽  
Temporal Dynamics ◽  
The United States ◽  
Mortality Statistics ◽  
Race Ethnicity ◽  
The Impact

Abstract This paper investigates the mortality statistics of the COVID-19 pandemic from the United States perspective. Using empirical data analysis and statistical inference tools, we bring out several exciting and important aspects of the pandemic, otherwise hidden. Specific patterns seen in demo- graphics such as race/ethnicity and age are discussed both qualitatively and quantitatively. We also study the role played by factors such as population density. Connections between COVID-19 and other respiratory diseases are also covered in detail. The temporal dynamics of the COVID-19 outbreak and the impact of vaccines in controlling the pandemic are also looked at with suf- ficient rigor. It is hoped that statistical inference such as the ones gathered in this paper would be helpful for better scientific understanding, policy prepa- ration and thus adequately preparing, should a similar situation arise in the future.

Brain Rhythms Provide a Framework for Neural Syntax

2019 ◽  
pp. 141-164
Author(s):  
György Buzsáki
Keyword(s):  
Information Exchange ◽  
Syntactic Structure ◽  
Fundamental Aspect ◽  
Brain Oscillations ◽  
Neuronal Oscillations ◽  
Brain Areas ◽  
Music Production ◽  
Hierarchical Nature ◽  
Neuronal Oscillators ◽  
Neuronal Computation

Brain oscillations are present in the same form in all mammals and represent a fundamental aspect of neuronal computation, including the generation of movement patterns, speech, and music production. Neuronal oscillators readily entrain each other, making the exchange of messages between brain areas effective. Because all neuronal oscillations are based on inhibition, they can parse and concatenate neuronal messages, a prerequisite for any coding mechanism. This chapter discusses how the hierarchical nature of cross-frequency–coupled rhythms can serve as a scaffold for combining neuronal letters into words and words into sentences, thus providing a syntactic structure for information exchange.

scholarly journals Dynamic Language Models for Streaming Text

2014 ◽  
Vol 2 ◽  
pp. 181-192 ◽  
Author(s):  
Dani Yogatama ◽  
Chong Wang ◽  
Bryan R. Routledge ◽  
Noah A. Smith ◽  
Eric P. Xing
Keyword(s):  
Social Media ◽  
Temporal Dynamics ◽  
Language Model ◽  
Language Modeling ◽  
Streaming Data ◽  
Language Models ◽  
Linguistic Context ◽  
Text Data ◽  
Competing Models ◽  
Context Features

We present a probabilistic language model that captures temporal dynamics and conditions on arbitrary non-linguistic context features. These context features serve as important indicators of language changes that are otherwise difficult to capture using text data by itself. We learn our model in an efficient online fashion that is scalable for large, streaming data. With five streaming datasets from two different genres—economics news articles and social media—we evaluate our model on the task of sequential language modeling. Our model consistently outperforms competing models.

Brightness Filling-in of Natural Images

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 136-136
Author(s):  
J H Elder
Keyword(s):  
Computational Model ◽  
Surface Brightness ◽  
Temporal Dynamics ◽  
Scale Space ◽  
Natural Images ◽  
Original Image ◽  
Edge Point ◽  
Contour Representation ◽  
Psychophysical Studies ◽  
Space Method

There is both psychophysical and physiological evidence that the perception of brightness variations in an image may be the result of a filling-in process in which the luminance signal is encoded only at image contours and is then neurally diffused to form representations of surface brightness. Despite this evidence, the filling-in hypothesis remains controversial. One problem is that in previous experiments highly simplified synthetic stimuli have been used; it is unclear whether brightness filling-in is feasible for complex natural images containing shading, shadows, and focal blur. To address this question, we present a computational model for brightness filling-in and results of experiments which test the model on a large and diverse set of natural images. The model is based on a scale-space method for edge detection which computes a contour code consisting of estimates of position, brightness, contrast, and blur at each edge point in an image (Elder and Zucker, 1996, paper presented at ECCV). This representation is then inverted by a diffusion-based filling-in algorithm which reconstructs an estimate of the original image. Psychophysical assessment of results shows that while filling-in of brightness alone leads to significant artifact, parallel filling-in of both brightness and blur produces perceptually accurate reconstructions. The temporal dynamics of blur reconstruction predicted by the model are consistent with psychophysical studies of blur perception (Westheimer, 1991 Journal of the Optical Society of America A8 681 – 685). These results suggest that a scale-adaptive contour representation can in principle capture the information needed for the perceptually accurate filling-in of complex natural images.

scholarly journals The separation of auditory experience and the temporal structure of MEG recorded brain activity

2017 ◽  
Author(s):  
Chris Allen
Keyword(s):  
High Frequency ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Temporal Structure ◽  
Oscillatory Activity ◽  
Auditory Stimuli ◽  
Brain Oscillations ◽  
Successful Separation ◽  
Auditory Experience ◽  
Oscillatory Brain Activity

AbstractDo brain oscillations limit the temporal dynamics of experience? This pre-registered study used the separation of auditory stimuli to track perceptual experience and related this to oscillatory activity using magnetoencephalography. The rates at which auditory stimuli could be individuated matched the rates of oscillatory brain activity. Stimuli also entrained brain activity at the frequencies at which they were presented and a progression of high frequency gamma band events appeared to predict successful separation. These findings support a generalised function for brain oscillations, across frequency bands, in the alignment of activity to delineate representations.

A computational model of spatio-temporal dynamics in depth filling-in

2004 ◽  
Vol 17 (2) ◽  
pp. 159-163 ◽  
Author(s):  
Shigeaki Nishina ◽  
Mitsuo Kawato
Keyword(s):  
Computational Model ◽  
Temporal Dynamics ◽  
Spatio Temporal

scholarly journals The same ultra-rapid parallel brain dynamics underpin the production and perception of speech

2021 ◽  
Author(s):  
Amie Fairs ◽  
Amandine Michelas ◽  
Sophie Dufour ◽  
Kristof Strijkers
Keyword(s):  
Language Processing ◽  
Time Course ◽  
Linear Models ◽  
Temporal Dynamics ◽  
Building Blocks ◽  
Naming Task ◽  
Stimulus Onset ◽  
Language Models ◽  
Mixed Linear Models ◽  
Listening Task

AbstractThe temporal dynamics by which linguistic information becomes available is one of the key properties to understand how language is organised in the brain. An unresolved debate between different brain language models is whether words, the building blocks of language, are activated in a sequential or parallel manner. In this study we approached this issue from a novel perspective by directly comparing the time course of word component activation in speech production versus perception. In an overt object naming task and a passive listening task we analysed with mixed linear models at the single-trial level the event-related brain potentials elicited by the same lexico-semantic and phonological word knowledge in the two language modalities. Results revealed that both word components manifested simultaneously as early as 75 ms after stimulus onset in production and perception; differences between the language modalities only became apparent after 300 ms of processing. The data provide evidence for ultra-rapid parallel dynamics of language processing and are interpreted within a neural assembly framework where words recruit the same integrated cell assemblies across production and perception. These word assemblies ignite early on in parallel and only later on reverberate in a behaviour-specific manner.

Sign in / Sign up

Export Citation Format

Share Document