scholarly journals What Neural Oscillations Can(not) Do for Syntactic Structure Building

2021 ◽  
Author(s):  
Nina Kazanina ◽  
Alessandro Tavano
Keyword(s):  
Information Integration ◽  
Syntactic Structure ◽  
Neural Mechanisms ◽  
Neural Oscillations ◽  
Multi Scale ◽  
Structure Building ◽  
Exact Function ◽  
Starting Point ◽  
Hierarchical Nature ◽  
Neurophysiological Basis

Understanding what someone says requires relating words in the sentence to one another as instructed by grammatical rules of language. In recent years, a neurophysiological basis for this process has become a prominent topic of discussion in cognitive neuroscience. Current proposals about the neural mechanisms of syntactic structure building converge in assigning a key role to neural oscillations but differ in the exact function assigned to them. We discuss two types of approaches – oscillations for chunking and oscillations for multi-scale information integration – and evaluate their merits and limitations considering a fundamentally hierarchical nature of syntactic representations in natural language. We highlight insights that can provide a tangible starting point for a wide-scope neurocognitive model of syntactic structure building.

The Neurophysiological Basis of the Auditory Continuity Illusion: A Mismatch Negativity Study

2003 ◽  
Vol 15 (5) ◽  
pp. 747-758 ◽  
Author(s):  
Christophe Micheyl ◽  
Robert P. Carlyon ◽  
Yury Shtyrov ◽  
Olaf Hauk ◽  
Tara Dodson ◽  
...  
Keyword(s):  
Mismatch Negativity ◽  
Sudden Change ◽  
Frequency Region ◽  
Neural Mechanisms ◽  
Oddball Paradigm ◽  
Standard Type ◽  
Upper Limit ◽  
Noise Frequency ◽  
Filtered Noise ◽  
Neurophysiological Basis

A sound turned off for a short moment can be perceived as continuous if the silent gap is filled with noise. The neural mechanisms underlying this “continuity illusion” were investigated using the mismatch negativity (MMN), an eventrelated potential reflecting the perception of a sudden change in an otherwise regular stimulus sequence. The MMN was recorded in four conditions using an oddball paradigm. The standards consisted of 500-Hz, 120-msec tone pips that were either physically continuous (Condition 1) or were interrupted by a 40-msec silent gap (Condition 2). The deviants consisted of the interrupted tone, but with the silent gap filled by a burst of bandpass-filtered noise. The noise either occupied the same frequency region as the tone and elicited the continuity illusion (Conditions 1a and 2a), or occupied a remote frequency region and did not elicit the illusion (Conditions 1b and 2b). We predicted that, if the continuity illusion is determined before MMN generation, then, other things being equal, the MMN should be larger in conditions where the deviants are perceived as continuous and the standards as interrupted or vice versa, than when both were perceived as continuous or both interrupted. Consistent with this prediction, we observed an interaction between standard type and noise frequency region, with the MMN being larger in Condition 1a than in Condition 1b, but smaller in Condition 2a than in Condition 2b. Because the subjects were instructed to ignore the tones and watch a silent movie during the recordings, the results indicate that the continuity illusion can occur outside the focus of attention. Furthermore, the latency of the MMN (less than approximately 200 msec postdeviance onset) places an upper limit on the stage of neural processing responsible for the illusion.

scholarly journals Revisiting the CompCars Dataset for Hierarchical Car Classification: New Annotations, Experiments, and Results

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 596
Author(s):  
Marco Buzzelli ◽  
Luca Segantin
Keyword(s):  
Real World ◽  
High Performance ◽  
Ad Hoc ◽  
Future Research ◽  
Levels Of Detail ◽  
Excellent Starting Point ◽  
Starting Point ◽  
Hierarchical Nature ◽  
Multiple Levels ◽  
Entire Dataset

We address the task of classifying car images at multiple levels of detail, ranging from the top-level car type, down to the specific car make, model, and year. We analyze existing datasets for car classification, and identify the CompCars as an excellent starting point for our task. We show that convolutional neural networks achieve an accuracy above 90% on the finest-level classification task. This high performance, however, is scarcely representative of real-world situations, as it is evaluated on a biased training/test split. In this work, we revisit the CompCars dataset by first defining a new training/test split, which better represents real-world scenarios by setting a more realistic baseline at 61% accuracy on the new test set. We also propagate the existing (but limited) type-level annotation to the entire dataset, and we finally provide a car-tight bounding box for each image, automatically defined through an ad hoc car detector. To evaluate this revisited dataset, we design and implement three different approaches to car classification, two of which exploit the hierarchical nature of car annotations. Our experiments show that higher-level classification in terms of car type positively impacts classification at a finer grain, now reaching 70% accuracy. The achieved performance constitutes a baseline benchmark for future research, and our enriched set of annotations is made available for public download.

Recent Development in Multiscale Simulation of Mechanical Properties at Material Interface

2010 ◽  
Vol 146-147 ◽  
pp. 491-494
Author(s):  
Ning Bo Liao ◽  
Miao Zhang ◽  
Rui Jiang
Keyword(s):  
Mechanical Properties ◽  
Multiscale Model ◽  
Multiscale Simulation ◽  
Material Interfaces ◽  
Material Interface ◽  
Multi Scale ◽  
Simulation Techniques ◽  
Hierarchical Nature ◽  
Feature Scale

For nanoscale devices and structures, interface phenomena often dominate their overall thermal behavior. The feature scale of material interfaces usually originate from nanometer length and present a hierarchical nature. Considering to the limitations of the continuum mechanics on the characterization of nano-scale, the multiscale model featuring the interface could be very important in materials design. The purpose of this review is to discuss the applications of multiscale modeling and simulation techniques to study the mechanical properties at materials interface. It is concluded that a multi-scale scheme is needed for this study due to the hierarchical characteristics of interface.

scholarly journals Construction of multi-scale grid for massive land survey data

2020 ◽  
Vol 206 ◽  
pp. 03018
Author(s):  
Jia Zhang ◽  
Xiulian Wang ◽  
Xiaotong Zhang ◽  
Xiaofei Bai ◽  
Qiang Chen
Keyword(s):  
Information Integration ◽  
Data Model ◽  
Spatiotemporal Data ◽  
Distributed Data ◽  
Data Organization ◽  
Vector Data ◽  
Grid Data ◽  
Geospatial Information ◽  
Multi Scale ◽  
Scale Grid

In the face of ever-growing and complex massive multi-source spatiotemporal data, the traditional vector data model is increasingly difficult to meet the needs of efficient data organization, management, calculation and analysis. Based on the simple and widely used geographic grid data organization model, this paper designs a technical method to convert vector data into multi-scale grid data, establishes a unified, standardized and seamless land spatial grid data model, and analyses the area accuracy of multi-scale grid data. Practice shows that the model can better meet the needs of multi-scale geospatial information integration and analysis, and it is easy to carry out distributed data processing, which provides technical support for the efficient organization, fusion and analysis of spatiotemporal data.

Algebraic combinatorics and QFT

2021 ◽  
pp. 76-94
Author(s):  
Adrian Tanasa
Keyword(s):  
Hopf Algebra ◽  
Hochschild Cohomology ◽  
Algebraic Combinatorics ◽  
Fourth Section ◽  
Analytic Combinatorics ◽  
Multi Scale ◽  
Starting Point ◽  
Feynman Graphs ◽  
Perturbative Renormalization

We have seen in the previous chapter some of the non-trivial interplay between analytic combinatorics and QFT. In this chapter, we illustrate how yet another branch of combinatorics, algebraic combinatorics, interferes with QFT. In this chapter, after a brief algebraic reminder in the first section, we introduce in the second section the Connes–Kreimer Hopf algebra of Feynman graphs and we show its relation with the combinatorics of QFT perturbative renormalization. We then study the algebra's Hochschild cohomology in relation with the combinatorial Dyson–Schwinger equation in QFT. In the fourth section we present a Hopf algebraic description of the so-called multi-scale renormalization (the multi-scale approach to the perturbative renormalization being the starting point for the constructive renormalization programme).

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.

Consciousness, awareness of insight and neural mechanisms of schizophrenia

2015 ◽  
Vol 26 (3) ◽  
Author(s):  
Tereza Touskova ◽  
Petr Bob
Keyword(s):  
Information Integration ◽  
Conscious Experience ◽  
Critical Role ◽  
Neural Mechanisms ◽  
The Other ◽  
Self Awareness ◽  
Synchronous Activity ◽  
Self Monitoring ◽  
Mental Integration ◽  
The Brain

AbstractAccording to recent research, disturbances of self-awareness and conscious experience have a critical role in the pathophysiology of schizophrenia, and in this context, schizophrenia is currently understood as a disorder characterized by distortions of acts of awareness, self-consciousness, and self-monitoring. Together, these studies suggest that the processes of disrupted awareness and conscious disintegration in schizophrenia might be related and represented by similar disruptions on the brain level, which, in principle, could be explained by various levels of disturbed connectivity and information disintegration that may negatively affect usual patterns of synchronous activity constituting adaptive integrative functions of consciousness. On the other hand, mental integration based on self-awareness and insight may significantly increase information integration and directly influence neural mechanisms underlying basic pathophysiological processes in schizophrenia.

The Integration of Sports Information in Personalized Network

2014 ◽  
Vol 644-650 ◽  
pp. 3124-3128
Author(s):  
Huan Zhao ◽  
Qiu Hong Wu ◽  
Jian Bo Zhao
Keyword(s):  
Information Integration ◽  
Information Service ◽  
Digital Information ◽  
Starting Point ◽  
Sports Information ◽  
The Individual

Digital information service is an important topic, which is based on the personalized information service, therefore, this paper takes individualized information service and information integration as the starting point to analyze and discuss the reason of information integration. Moreover, it takes the individual requirements of users as the basis according to the user-oriented principle, putting forward the concept of personalized information integration.

scholarly journals Neurophysiological Basis of Multi-Scale Entropy of Brain Complexity and Its Relationship With Functional Connectivity

2018 ◽  
Vol 12 ◽  
Author(s):  
Danny J. J. Wang ◽  
Kay Jann ◽  
Chang Fan ◽  
Yang Qiao ◽  
Yu-Feng Zang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Multi Scale ◽  
Neurophysiological Basis

scholarly journals Decoding the neural mechanisms of human tool use

eLife ◽  
2013 ◽  
Vol 2 ◽  
Author(s):  
Jason P Gallivan ◽  
D Adam McLean ◽  
Kenneth F Valyear ◽  
Jody C Culham
Keyword(s):  
Tool Use ◽  
Neural Mechanisms ◽  
Primate Species ◽  
Classification Methods ◽  
Body Perception ◽  
Sensorimotor Processing ◽  
Neural Representations ◽  
Frontoparietal Cortex ◽  
Hierarchical Nature ◽  
The Brain

Sophisticated tool use is a defining characteristic of the primate species but how is it supported by the brain, particularly the human brain? Here we show, using functional MRI and pattern classification methods, that tool use is subserved by multiple distributed action-centred neural representations that are both shared with and distinct from those of the hand. In areas of frontoparietal cortex we found a common representation for planned hand- and tool-related actions. In contrast, in parietal and occipitotemporal regions implicated in hand actions and body perception we found that coding remained selectively linked to upcoming actions of the hand whereas in parietal and occipitotemporal regions implicated in tool-related processing the coding remained selectively linked to upcoming actions of the tool. The highly specialized and hierarchical nature of this coding suggests that hand- and tool-related actions are represented separately at earlier levels of sensorimotor processing before becoming integrated in frontoparietal cortex.

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