scholarly journals Evidence for a deep, distributed and dynamic code for animacy in human ventral anterior temporal cortex

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Timothy T Rogers ◽  
Christopher R Cox ◽  
Qihong Lu ◽  
Akihiro Shimotake ◽  
Takayuki Kikuch ◽  
...  
Keyword(s):  
Neural Network ◽  
Semantic Information ◽  
Semantic Network ◽  
Temporal Cortex ◽  
Semantic Features ◽  
Temporal Lobes ◽  
Correct Information ◽  
Neural Populations ◽  
Ventral Temporal Cortex ◽  
Multivariate Pattern

How does the human brain encode semantic information about objects? This paper reconciles two seemingly contradictory views. The first proposes that local neural populations independently encode semantic features; the second, that semantic representations arise as a dynamic distributed code that changes radically with stimulus processing. Combining simulations with a well-known neural network model of semantic memory, multivariate pattern classification, and human electrocorticography, we find that both views are partially correct: information about the animacy of a depicted stimulus is distributed across ventral temporal cortex in a dynamic code possessing feature-like elements posteriorly but with elements that change rapidly and nonlinearly in anterior regions. This pattern is consistent with the view that anterior temporal lobes serve as a deep cross-modal ‘hub’ in an interactive semantic network, and more generally suggests that tertiary association cortices may adopt dynamic distributed codes difficult to detect with common brain imaging methods.

scholarly journals Evidence for a deep, distributed and dynamic semantic code in human ventral anterior temporal cortex

2019 ◽  
Author(s):  
Timothy T. Rogers ◽  
Christopher Cox ◽  
Qihong Lu ◽  
Akihiro Shimotake ◽  
Takayuki Kikuch ◽  
...  
Keyword(s):  
Semantic Information ◽  
Semantic Network ◽  
Temporal Cortex ◽  
Semantic Features ◽  
Dynamic Semantic ◽  
Temporal Lobes ◽  
Neural Populations ◽  
Ventral Temporal Cortex ◽  
Multivariate Pattern ◽  
Stable Feature

AbstractHow does the human brain encode semantic information about objects? This paper reconciles two seemingly contradictory views. The first proposes that local neural populations independently encode semantic features; the second, that semantic representations arise as a dynamic distributed code that changes radically with stimulus processing. Combining simulations with a well-known neural network model of semantic memory, multivariate pattern classification, and human electrocorticography, we find that both views are partially correct: semantic information is distributed across ventral temporal cortex in a dynamic code that possesses stable feature-like elements in posterior regions but with elements that change rapidly and nonlinearly in anterior regions. This pattern is consistent with the view that anterior temporal lobes serve as a deep cross-modal “hub” in an interactive semantic network, and more generally suggests that tertiary association cortices may adopt dynamic distributed codes difficult to detect with common brain imaging methods.

scholarly journals Bert-Enhanced Text Graph Neural Network for Classification

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1536
Author(s):  
Yiping Yang ◽  
Xiaohui Cui
Keyword(s):  
Neural Network ◽  
Semantic Information ◽  
Structural Information ◽  
Text Processing ◽  
Language Model ◽  
Research Direction ◽  
Semantic Features ◽  
Structure Information ◽  
Single Text ◽  
Graph Neural Networks

Text classification is a fundamental research direction, aims to assign tags to text units. Recently, graph neural networks (GNN) have exhibited some excellent properties in textual information processing. Furthermore, the pre-trained language model also realized promising effects in many tasks. However, many text processing methods cannot model a single text unit’s structure or ignore the semantic features. To solve these problems and comprehensively utilize the text’s structure information and semantic information, we propose a Bert-Enhanced text Graph Neural Network model (BEGNN). For each text, we construct a text graph separately according to the co-occurrence relationship of words and use GNN to extract text features. Moreover, we employ Bert to extract semantic features. The former part can take into account the structural information, and the latter can focus on modeling the semantic information. Finally, we interact and aggregate these two features of different granularity to get a more effective representation. Experiments on standard datasets demonstrate the effectiveness of BEGNN.

scholarly journals A Novel Approach for Malicious URL Detection Based on the Joint Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
JianTing Yuan ◽  
YiPeng Liu ◽  
Long Yu
Keyword(s):  
Neural Network ◽  
Semantic Information ◽  
Attention Mechanism ◽  
Semantic Features ◽  
Uniform Resource Locator ◽  
Word Level ◽  
Network Algorithm ◽  
Model Combining ◽  
Novel Approach ◽  
Neural Network Algorithm

The number of malicious websites is increasing yearly, and many companies and individuals worldwide have suffered losses. Therefore, the detection of malicious websites is a task that needs continuous development. In this study, a joint neural network algorithm model combining the attention mechanism, bidirectional independent recurrent neural network (Bi-IndRNN), and capsule network (CapsNet) is proposed. The word vector tool word2vec trains the character- and word-level uniform resource locator (URL) static embedding vector features. At the same time, the algorithm will also extract texture fingerprint features that can compare the content differences of different malicious web URL binary files. Then, the extracted features are fused and input into the joint neural network algorithm model. First, the multihead attention mechanism is used to extract contextual semantic features by adjusting weights and Bi-IndRNN. Second, CapsNet with dynamic routing is used to extract deep semantic information. Finally, the sigmoid classifier is used for classification. This study uses different methods from different angles to extract more comprehensive features. From the experimental results, the method proposed in this study improves the classification accuracy of malicious web page detection compared with other researchers.

What Evidence Supports Special Processing for Faces? A Cautionary Tale for fMRI Interpretation

2013 ◽  
Vol 25 (11) ◽  
pp. 1777-1793 ◽  
Author(s):  
Rosemary A. Cowell ◽  
Garrison W. Cottrell
Keyword(s):  
Face Processing ◽  
Temporal Cortex ◽  
Cautionary Tale ◽  
Face Area ◽  
Fmri Study ◽  
Topography Model ◽  
Ventral Temporal Cortex ◽  
Object Form ◽  
Multivariate Pattern ◽  
Processing Mechanisms

We trained a neurocomputational model on six categories of photographic images that were used in a previous fMRI study of object and face processing. Multivariate pattern analyses of the activations elicited in the object-encoding layer of the model yielded results consistent with two previous, contradictory fMRI studies. Findings from one of the studies [Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293, 2425–2430, 2001] were interpreted as evidence for the object-form topography model. Findings from the other study [Spiridon, M., & Kanwisher, N. How distributed is visual category information in human occipito-temporal cortex? An fMRI study. Neuron, 35, 1157–1165, 2002] were interpreted as evidence for neural processing mechanisms in the fusiform face area that are specialized for faces. Because the model contains no special processing mechanism or specialized architecture for faces and yet it can reproduce the fMRI findings used to support the claim that there are specialized face-processing neurons, we argue that these fMRI results do not actually support that claim. Results from our neurocomputational model therefore constitute a cautionary tale for the interpretation of fMRI data.

scholarly journals Attention selectively reshapes the geometry of distributed semantic representation

2016 ◽  
Author(s):  
Samuel A. Nastase ◽  
Andrew C. Connolly ◽  
Nikolaas N. Oosterhof ◽  
Yaroslav O. Halchenko ◽  
J. Swaroop Guntupalli ◽  
...  
Keyword(s):  
Animal Behavior ◽  
Semantic Representation ◽  
Temporal Cortex ◽  
Neural Representation ◽  
Natural Environments ◽  
Complex Stimulus ◽  
Semantic Features ◽  
Response Patterns ◽  
Brain Extracts ◽  
Ventral Temporal Cortex

AbstractHumans prioritize different semantic qualities of a complex stimulus depending on their behavioral goals. These semantic features are encoded in distributed neural populations, yet it is unclear how attention might operate across these distributed representations. To address this, we presented participants with naturalistic video clips of animals behaving in their natural environments while the participants attended to either behavior or taxonomy. We used models of representational geometry to investigate how attentional allocation affects the distributed neural representation of animal behavior and taxonomy. Attending to animal behavior transiently increased the discriminability of distributed population codes for observed actions in anterior intraparietal, pericentral, and ventral temporal cortices. Attending to animal taxonomy while viewing the same stimuli increased the discriminability of distributed animal category representations in ventral temporal cortex. For both tasks, attention selectively enhanced the discriminability of response patterns along behaviorally relevant dimensions. These findings suggest that behavioral goals alter how the brain extracts semantic features from the visual world. Attention effectively disentangles population responses for downstream read-out by sculpting representational geometry in late-stage perceptual areas.

A Comparison Study of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition

2005 ◽  
Author(s):  
Jungeui Hong ◽  
Elizabeth A. Cudney ◽  
Genichi Taguchi ◽  
Rajesh Jugulum ◽  
Kioumars Paryani ◽  
...  
Keyword(s):  
Neural Network ◽  
Small Data ◽  
Data Sets ◽  
Comparison Study ◽  
Data Set ◽  
Set Size ◽  
Breast Cancer Study ◽  
Discriminant Ability ◽  
Small Data Sets ◽  
Multivariate Pattern

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis-Taguchi System and a neural network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.

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