scholarly journals Biased neural representation of feature-based attention in the human brain

2019 ◽  
Author(s):  
Mengyuan Gong ◽  
Taosheng Liu
Keyword(s):  
Compound Stimulus ◽  
Neural Representation ◽  
Gradual Transition ◽  
Neural Responses ◽  
Brain Areas ◽  
Cortical Hierarchy ◽  
Feature Based ◽  
Efficient Processing ◽  
Low Dimensional ◽  
High Level

AbstractSelective attention is a core cognitive function for efficient processing of information. Although it is well known that attention can modulate neural responses in many brain areas, the computational principles underlying attentional modulation remain unclear. Contrary to the prevailing view of a high-dimensional, distributed neural representation, here we show a surprisingly simple, biased neural representation for feature-based attention in a large dataset including five human fMRI studies. We found that when participants selected one feature from a compound stimulus, voxels in many cortical areas responded consistently higher to one attended feature over the other. This univariate bias was robust at the level of single brain areas and consistent across brain areas within individual subjects. Importantly, this univariate bias showed a progressively stronger magnitude along the cortical hierarchy. In frontoparietal areas, the bias was strongest and contributed largely to pattern-based decoding, whereas early visual areas lacked such a bias. These findings suggest a gradual transition from a more analog to a more abstract representation of attentional priority along the cortical hierarchy. Biased neural responses in high-level areas likely reflect a low-dimensional neural code that facilitates robust representation and simple read-out of cognitive variables.

scholarly journals Mixed selectivity coding of sensory and motor social signals in the thalamus of a weakly electric fish

2021 ◽  
Author(s):  
Avner Wallach ◽  
Alexandre Melanson ◽  
Andre Longtin ◽  
Len Maler
Keyword(s):  
Social Behavior ◽  
Mate Selection ◽  
Electric Fish ◽  
Neural Representation ◽  
Weakly Electric Fish ◽  
Social Signals ◽  
Communication Signals ◽  
Low Dimensional ◽  
High Level ◽  
Weakly Electric

Recent studies have shown that high-level neural activity often exhibits mixed selectivity to multi-variate signals. How such representations arise and how they modulate natural behavior is poorly understood. The social behavior of weakly electric fish is relatively low-dimensional and easily reproduced in the laboratory. Here we show how electrosensory signals related to courtship and rivalry in Apteronotus leptorhynchus are represented in the preglomerular nucleus, the thalamic region exclusively connecting the midbrain with the pallium. We show that preglomerular cells convert their midbrain inputs into a mixed selectivity code that includes corollary discharge of outgoing communication signals. We discuss how the preglomerular pallial targets might use these inputs to control social behavior and determine dominance in male-male competition and female mate selection during courtship. Our results showcase the potential of the electrocommunication system as an accessible model for studying the neural substrates of social behavior and principles of multi-dimensional neural representation.

scholarly journals A Flexible Neural Representation of Faces in the Human Brain

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Runnan Cao ◽  
Xin Li ◽  
Alexander Todorov ◽  
Shuo Wang
Keyword(s):  
Human Brain ◽  
Visual Processing ◽  
Brain Regions ◽  
Neural Representation ◽  
Neural Responses ◽  
Low Level ◽  
Feature Vectors ◽  
Social Traits ◽  
Task Instructions ◽  
High Level

Abstract An important question in human face perception research is to understand whether the neural representation of faces is dynamically modulated by context. In particular, although there is a plethora of neuroimaging literature that has probed the neural representation of faces, few studies have investigated what low-level structural and textural facial features parametrically drive neural responses to faces and whether the representation of these features is modulated by the task. To answer these questions, we employed 2 task instructions when participants viewed the same faces. We first identified brain regions that parametrically encoded high-level social traits such as perceived facial trustworthiness and dominance, and we showed that these brain regions were modulated by task instructions. We then employed a data-driven computational face model with parametrically generated faces and identified brain regions that encoded low-level variation in the faces (shape and skin texture) that drove neural responses. We further analyzed the evolution of the neural feature vectors along the visual processing stream and visualized and explained these feature vectors. Together, our results showed a flexible neural representation of faces for both low-level features and high-level social traits in the human brain.

scholarly journals Cortical response to naturalistic stimuli is largely predictable with deep neural networks

2021 ◽  
Vol 7 (22) ◽  
pp. eabe7547
Author(s):  
Meenakshi Khosla ◽  
Gia H. Ngo ◽  
Keith Jamison ◽  
Amy Kuceyeski ◽  
Mert R. Sabuncu
Keyword(s):  
Brain Function ◽  
Deep Neural Networks ◽  
Neural Responses ◽  
Dynamic Interactions ◽  
Hierarchical Processing ◽  
Human Connectome Project ◽  
Neural Information ◽  
Neural Information Processing ◽  
Visual Interactions ◽  
High Level

Naturalistic stimuli, such as movies, activate a substantial portion of the human brain, invoking a response shared across individuals. Encoding models that predict neural responses to arbitrary stimuli can be very useful for studying brain function. However, existing models focus on limited aspects of naturalistic stimuli, ignoring the dynamic interactions of modalities in this inherently context-rich paradigm. Using movie-watching data from the Human Connectome Project, we build group-level models of neural activity that incorporate several inductive biases about neural information processing, including hierarchical processing, temporal assimilation, and auditory-visual interactions. We demonstrate how incorporating these biases leads to remarkable prediction performance across large areas of the cortex, beyond the sensory-specific cortices into multisensory sites and frontal cortex. Furthermore, we illustrate that encoding models learn high-level concepts that generalize to task-bound paradigms. Together, our findings underscore the potential of encoding models as powerful tools for studying brain function in ecologically valid conditions.

Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia

2021 ◽  
pp. 1-14
Author(s):  
Debo Dong ◽  
Dezhong Yao ◽  
Yulin Wang ◽  
Seok-Jun Hong ◽  
Sarah Genon ◽  
...  
Keyword(s):  
Sensory Information ◽  
Resting State Fmri ◽  
Brain Regions ◽  
High Order ◽  
Hierarchical Organization ◽  
System Level ◽  
Integrative System ◽  
Sensorimotor Region ◽  
Cortical Hierarchy ◽  
High Level

Abstract Background Schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. Methods We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state fMRI to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients v. 122 controls). Results We demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal−parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and fronto-parietal and ventral attention regions along the classical sensation-to-cognition continuum (voxel-level corrected, p < 0.05). Conclusions The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascading impairments from the disruption of the somatosensory−motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

scholarly journals Quantifying the signals contained in heterogeneous neural responses and determining their relationships with task performance

2014 ◽  
Vol 112 (6) ◽  
pp. 1584-1598 ◽  
Author(s):  
Marino Pagan ◽  
Nicole C. Rust
Keyword(s):  
Task Performance ◽  
Single Neuron ◽  
Weighted Sums ◽  
Neural Responses ◽  
Signal Modulation ◽  
Neural Data ◽  
Match To Sample ◽  
Neural Populations ◽  
Different Types ◽  
High Level

The responses of high-level neurons tend to be mixtures of many different types of signals. While this diversity is thought to allow for flexible neural processing, it presents a challenge for understanding how neural responses relate to task performance and to neural computation. To address these challenges, we have developed a new method to parse the responses of individual neurons into weighted sums of intuitive signal components. Our method computes the weights by projecting a neuron's responses onto a predefined orthonormal basis. Once determined, these weights can be combined into measures of signal modulation; however, in their raw form these signal modulation measures are biased by noise. Here we introduce and evaluate two methods for correcting this bias, and we report that an analytically derived approach produces performance that is robust and superior to a bootstrap procedure. Using neural data recorded from inferotemporal cortex and perirhinal cortex as monkeys performed a delayed-match-to-sample target search task, we demonstrate how the method can be used to quantify the amounts of task-relevant signals in heterogeneous neural populations. We also demonstrate how these intuitive quantifications of signal modulation can be related to single-neuron measures of task performance ( d′).

Feature Based Comparison and Selection of SDN Controller

2019 ◽  
Vol 16 (05) ◽  
pp. 1950029
Author(s):  
Mohammed Abdul Rahman AlShehri ◽  
Shailendra Mishra
Keyword(s):  
Network Performance ◽  
Campus Network ◽  
Analytic Hierarchy ◽  
Decision Logic ◽  
Feature Based ◽  
Efficient Processing ◽  
Hierarchy Process ◽  
Entire Network ◽  
Sdn Controller ◽  
Selection Of

Software defined network (SDN) controller selection in SDN is a key challenge to the network administrator. In SDN, control plane is an isolated process and operate on control layer. The controller provides a universal view of the entire network and support applications and services. The three focused parameters for controller selection are productivity, campus network and open source. In SDN, it is vital to have a good device for the efficient processing of all requests made by the switch and for good behavior of the network. For selecting best controller for the specified parameters, decision logic has to be developed that allow us to do comparison of the available controllers. Therefore, in this research we have suggested a methodology that uses analytic-hierarchy-process (AHP) to find a best controller. The approach has been studied and verified for a big organization network setup of Al-Majmaah University, Saudi Arabia. The approach is found to be more effective and increase the network performance significantly.

The gabbro–dyke transition zone demonstrated on Tviberg, Solund–Stavfjord Ophiolite Complex

1996 ◽  
Vol 133 (5) ◽  
pp. 573-582 ◽  
Author(s):  
K. P. Skjerlie ◽  
H. Furnes
Keyword(s):  
Transition Zone ◽  
Basaltic Magma ◽  
Source Region ◽  
Geochemical Evolution ◽  
Dyke Swarm ◽  
Gradual Transition ◽  
Ophiolite Complex ◽  
Plutonic Complex ◽  
Plutonic Rocks ◽  
High Level

AbstractThe transition zone between 100 % dykes and high-level plutonic rocks of the Solund-Stavfjord Ophiolite Complex is complex due to the existence of many lithologies with different and variable contact relationships. The rocks of the plutonic complex vary in composition from FeTi basaltic to quartz dioritic, and the grain sizes vary from fine to pegmatitic. Felsic varieties are produced by fractional crystallization of basaltic magma as demonstrated by geochemical evolution and by gradual transition from gabbro to quartz diorite. Patches of fractionated dioritic rocks may show both gradual and intrusive relationships with the surrounding host gabbro. This demonstrates that late-stage liquids commonly left the source region and locally intruded the surrounding parent rocks. The high-level plutonic rocks are thoroughly epidotized and are cut by dykes consisting of granoblastic epidote and quartz. The high-level plutonic complex is associated with irregular bodies of fine- to medium-grained plagioclase-porphyritic diabase of high MgO content. These diabase bodies are intruded by dykes that become progressively more regular in shape. The plutonic complex locally shows intrusive relationships with the overlying 100% dyke complex, but is itself cut by two dyke swarms. The dykes of the first swarm formed while the plutonic complex experienced sinistral shear strain, and the dykes are generally less regular and thinner than the dykes of the second swarm. This indicates that the dykes of the first swarm intruded while the rocks of the plutonic complex were still hot, while the next dyke swarm intruded later when the rock complex was colder. Dykes of both swarms range in composition from slightly to strongly fractionated, suggesting that the magma chambers they were expelled from underwent significant fractionation in between magma replenishment. Numerous dykes of both swarms carry large quantities of glomeroporphyritic aggregates of plagioclase and altered clinopyroxene, indicating that the source area to the dykes very often was a crystal mush.

An Interactive Graphical Approach to Feature Modeling Using Halfspaces and Geometric Constraints

1990 ◽  
Author(s):  
Maarten J. G. M. van Emmerik
Keyword(s):  
Spatial Relations ◽  
Feature Modeling ◽  
Geometric Constraints ◽  
Boolean Combination ◽  
Graphical Approach ◽  
Geometric Characteristics ◽  
Feature Based ◽  
New Feature ◽  
High Level ◽  
And Control

Abstract Feature modeling enables the specification of a model with standardized high-level shape aspects that have a functional meaning for design or manufacturing. In this paper an interactive graphical approach to feature-based modeling is presented. The user can represent features as new CSG primitives, specified as a Boolean combination of halfspaces. Constraints between halfspaces specify the geometric characteristics of a feature and control feature validity. Once a new feature is defined and stored in a library, it can be used in other objects and positioned, oriented and dimensioned by direct manipulation with a graphics cursor. Constraints between features prevent feature interference and specify spatial relations between features.

scholarly journals Visual search for object categories is predicted by the representational architecture of high-level visual cortex

2017 ◽  
Vol 117 (1) ◽  
pp. 388-402 ◽  
Author(s):  
Michael A. Cohen ◽  
George A. Alvarez ◽  
Ken Nakayama ◽  
Talia Konkle
Keyword(s):  
Visual Cortex ◽  
Visual Search ◽  
Visual System ◽  
Visual Processing ◽  
Search Task ◽  
Visual Search Task ◽  
Visual Object ◽  
Neural Responses ◽  
Object Categories ◽  
High Level

Visual search is a ubiquitous visual behavior, and efficient search is essential for survival. Different cognitive models have explained the speed and accuracy of search based either on the dynamics of attention or on similarity of item representations. Here, we examined the extent to which performance on a visual search task can be predicted from the stable representational architecture of the visual system, independent of attentional dynamics. Participants performed a visual search task with 28 conditions reflecting different pairs of categories (e.g., searching for a face among cars, body among hammers, etc.). The time it took participants to find the target item varied as a function of category combination. In a separate group of participants, we measured the neural responses to these object categories when items were presented in isolation. Using representational similarity analysis, we then examined whether the similarity of neural responses across different subdivisions of the visual system had the requisite structure needed to predict visual search performance. Overall, we found strong brain/behavior correlations across most of the higher-level visual system, including both the ventral and dorsal pathways when considering both macroscale sectors as well as smaller mesoscale regions. These results suggest that visual search for real-world object categories is well predicted by the stable, task-independent architecture of the visual system. NEW & NOTEWORTHY Here, we ask which neural regions have neural response patterns that correlate with behavioral performance in a visual processing task. We found that the representational structure across all of high-level visual cortex has the requisite structure to predict behavior. Furthermore, when directly comparing different neural regions, we found that they all had highly similar category-level representational structures. These results point to a ubiquitous and uniform representational structure in high-level visual cortex underlying visual object processing.

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