scholarly journals Decision-related feedback in visual cortex lacks spatial selectivity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Katrina R. Quinn ◽  
Lenka Seillier ◽  
Daniel A. Butts ◽  
Hendrikje Nienborg
Keyword(s):  
Contextual Information ◽  
Relevant Information ◽  
Common Mechanism ◽  
Spatial Selectivity ◽  
Population Responses ◽  
Stimulus Choice ◽  
Visual Areas ◽  
Computational Work ◽  
Feature Based ◽  
The Brain

AbstractFeedback in the brain is thought to convey contextual information that underlies our flexibility to perform different tasks. Empirical and computational work on the visual system suggests this is achieved by targeting task-relevant neuronal subpopulations. We combine two tasks, each resulting in selective modulation by feedback, to test whether the feedback reflected the combination of both selectivities. We used visual feature-discrimination specified at one of two possible locations and uncoupled the decision formation from motor plans to report it, while recording in macaque mid-level visual areas. Here we show that although the behavior is spatially selective, using only task-relevant information, modulation by decision-related feedback is spatially unselective. Population responses reveal similar stimulus-choice alignments irrespective of stimulus relevance. The results suggest a common mechanism across tasks, independent of the spatial selectivity these tasks demand. This may reflect biological constraints and facilitate generalization across tasks. Our findings also support a previously hypothesized link between feature-based attention and decision-related activity.

Why Does the Brain Have So Many Visual Areas?

1989 ◽  
Vol 1 (2) ◽  
pp. 121-135 ◽  
Author(s):  
Jon H. Kaas
Keyword(s):  
Species Differences ◽  
Visual Area ◽  
Common Mechanism ◽  
Functional Requirements ◽  
Specific Stimulus ◽  
Stimulus Attribute ◽  
Cortical Areas ◽  
Visual Areas ◽  
Stimulus Parameters ◽  
The Brain

Mammals vary in number of visual areas from a few to 20 or more as a result of new visual areas being added to the middle levels of processing hierarchies. Having more visual areas probably increases visual abilities, perhaps in part by allowing more stimulus parameters to be considered. Proposals that each visual area computes and thereby “detects” a specific stimulus attribute have so far dealt with attributes that most mammals can detect and thus do not relate to the issue of species differences in numbers of areas. The problem of forming and maintaining complex patterns of interconnections between many different sets of distinct processing models within an area may limit multiplying functions within a field. In addition, the adding of new visual areas is a way of avoiding constraints on modifying existing visual areas that are imposed by the ongoing functional requirements. Thus, increasing the number of visual or other cortical areas is an effective and apparently common mechanism for evolving new capacities.

scholarly journals Individual differences in experienced and observational decision-making illuminate interactions between reinforcement learning and declarative memory

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Batel Yifrah ◽  
Ayelet Ramaty ◽  
Genela Morris ◽  
Avi Mendelsohn
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Declarative Memory ◽  
Contextual Information ◽  
Memory Performance ◽  
Relevant Information ◽  
Subjective Memory ◽  
Types Of Information ◽  
Reinforcement Learning Models ◽  
Implicit And Explicit

AbstractDecision making can be shaped both by trial-and-error experiences and by memory of unique contextual information. Moreover, these types of information can be acquired either by means of active experience or by observing others behave in similar situations. The interactions between reinforcement learning parameters that inform decision updating and memory formation of declarative information in experienced and observational learning settings are, however, unknown. In the current study, participants took part in a probabilistic decision-making task involving situations that either yielded similar outcomes to those of an observed player or opposed them. By fitting alternative reinforcement learning models to each subject, we discerned participants who learned similarly from experience and observation from those who assigned different weights to learning signals from these two sources. Participants who assigned different weights to their own experience versus those of others displayed enhanced memory performance as well as subjective memory strength for episodes involving significant reward prospects. Conversely, memory performance of participants who did not prioritize their own experience over others did not seem to be influenced by reinforcement learning parameters. These findings demonstrate that interactions between implicit and explicit learning systems depend on the means by which individuals weigh relevant information conveyed via experience and observation.

scholarly journals Recurrent Multi-Fiber Network for 3D MRI Brain Tumor Segmentation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 320
Author(s):  
Yue Zhao ◽  
Xiaoqiang Ren ◽  
Kun Hou ◽  
Wentao Li
Keyword(s):  
Brain Tumor ◽  
Network Architecture ◽  
Contextual Information ◽  
Computational Cost ◽  
Disease Diagnosis ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Fiber Network ◽  
3D Network ◽  
The Brain

Automated brain tumor segmentation based on 3D magnetic resonance imaging (MRI) is critical to disease diagnosis. Moreover, robust and accurate achieving automatic extraction of brain tumor is a big challenge because of the inherent heterogeneity of the tumor structure. In this paper, we present an efficient semantic segmentation 3D recurrent multi-fiber network (RMFNet), which is based on encoder–decoder architecture to segment the brain tumor accurately. 3D RMFNet is applied in our paper to solve the problem of brain tumor segmentation, including a 3D recurrent unit and 3D multi-fiber unit. First of all, we propose that recurrent units segment brain tumors by connecting recurrent units and convolutional layers. This quality enhances the model’s ability to integrate contextual information and is of great significance to enhance the contextual information. Then, a 3D multi-fiber unit is added to the overall network to solve the high computational cost caused by the use of a 3D network architecture to capture local features. 3D RMFNet combines both advantages from a 3D recurrent unit and 3D multi-fiber unit. Extensive experiments on the Brain Tumor Segmentation (BraTS) 2018 challenge dataset show that our RMFNet remarkably outperforms state-of-the-art methods, and achieves average Dice scores of 89.62%, 83.65% and 78.72% for the whole tumor, tumor core and enhancing tumor, respectively. The experimental results prove our architecture to be an efficient tool for brain tumor segmentation accurately.

scholarly journals Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

2018 ◽  
Vol 8 (9) ◽  
pp. 1632 ◽  
Author(s):  
Zahra Rezaei ◽  
Ali Selamat ◽  
Arash Taki ◽  
Mohd Mohd Rahim ◽  
Mohammed Abdul Kadir ◽  
...  
Keyword(s):  
Vulnerable Plaque ◽  
Plaque Rupture ◽  
Back Propagation ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Relevant Information ◽  
Back Propagation Neural Network ◽  
Common Mechanism ◽  
Support Vector ◽  
Virtual Histology

Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.

scholarly journals Efficient generation of reciprocal signals by inhibition

2012 ◽  
Vol 107 (9) ◽  
pp. 2453-2462 ◽  
Author(s):  
Sung-min Park ◽  
Esra Tara ◽  
Kamran Khodakhah
Keyword(s):  
Purkinje Cells ◽  
Granule Cell ◽  
Granule Cells ◽  
Mossy Fiber ◽  
Cell Activity ◽  
Common Mechanism ◽  
Input Output ◽  
Firing Rates ◽  
Neuronal Computation ◽  
The Brain

Reciprocal activity between populations of neurons has been widely observed in the brain and is essential for neuronal computation. The different mechanisms by which reciprocal neuronal activity is generated remain to be established. A common motif in neuronal circuits is the presence of afferents that provide excitation to one set of principal neurons and, via interneurons, inhibition to a second set of principal neurons. This circuitry can be the substrate for generation of reciprocal signals. Here we demonstrate that this equivalent circuit in the cerebellar cortex enables the reciprocal firing rates of Purkinje cells to be efficiently generated from a common set of mossy fiber inputs. The activity of a mossy fiber is relayed to Purkinje cells positioned immediately above it by excitatory granule cells. The firing rates of these Purkinje cells increase as a linear function of mossy fiber, and thus granule cell, activity. In addition to exciting Purkinje cells positioned immediately above it, the activity of a mossy fiber is relayed to laterally positioned Purkinje cells by a disynaptic granule cell → molecular layer interneuron pathway. Here we show in acutely prepared cerebellar slices that the input-output relationship of these laterally positioned Purkinje cells is linear and reciprocal to the first set. A similar linear input-output relationship between decreases in Purkinje cell firing and strength of stimulation of laterally positioned granule cells was also observed in vivo. Use of interneurons to generate reciprocal firing rates may be a common mechanism by which the brain generates reciprocal signals.

scholarly journals Neuroplasticity in visual impairments

2018 ◽  
Vol 10 (4) ◽  
Author(s):  
Paulo Ramiler Silva ◽  
Tiago Farias ◽  
Fernando Cascio ◽  
Levi Dos Santos ◽  
Vinícius Peixoto ◽  
...  
Keyword(s):  
Systematic Review ◽  
Visual Acuity ◽  
Visual Impairment ◽  
Visual Impairments ◽  
Cortical Reorganization ◽  
Pubmed Database ◽  
Visual Areas ◽  
Visual Acuity Loss ◽  
The Brain

The visual acuity loss enables the brain to access new pathways in the quest to overcome the visual limitation and this is wellknown as neuroplasticity which have mechanisms to cortical reorganization. In this review, we related the evidences about the neuroplasticity as well as cortical anatomical differences and functional repercussions in visual impairments. We performed a systematic review of PUBMED database, without date or status publication restrictions. The findings demonstrate that the visual impairment produce a compensatory sensorial effect, in which non-visual areas are related to both cross (visual congenital) and multimodal (late blind) neuroplasticity.

scholarly journals Topic Maps For Improving Services In Disaster Operations Management

2008 ◽  
Vol 1 (1) ◽  
pp. 83-92 ◽  
Author(s):  
G. Alan Wang ◽  
Christopher W. Zobel
Keyword(s):  
Operations Management ◽  
Information Exchange ◽  
Contextual Information ◽  
Relevant Information ◽  
Important Application ◽  
Service Science ◽  
Semantic Technologies ◽  
Application Area ◽  
Topic Maps ◽  
Preliminary Discussion

Disaster operations management is an increasingly important application area for the developing techniques of service science. This paper examines the use of topic maps, a semantic technology, within this environment, and provides a preliminary discussion of the benefits that its implementation can provide in the capture and exchange of contextual information. The discussion is motivated by a look at the different phases of disaster operations management in a services context, and focuses on the need for effective and relevant information exchange as an important part of the services process. As the amount and complexity of information increases within such processes, semantic technologies are becoming increasingly important as a means representing and managing contextual information. This paper seeks to help further the understanding of the relevance of such tools as part of the study of service science.

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