scholarly journals Selective neural coding of object, feature, and geometry spatial cues in humans

2021 ◽  
Author(s):  
Stephen Ramanoël ◽  
Marion Durteste ◽  
Alice Bizeul ◽  
Anthony Ozier-Lafontaine ◽  
Marcia Bécu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Spatial Coding ◽  
Spatial Cues ◽  
Human Navigation ◽  
Visual Spatial ◽  
Object Based ◽  
Feature Based ◽  
The Neural Networks ◽  
Object Feature ◽  
Neural Signatures

SummaryOrienting in space requires the processing and encoding of visual spatial cues. The dominant hypothesis about the brain structures mediating the coding of spatial cues stipulates the existence of a hippocampal-dependent system for the representation of geometry and a striatal-dependent system for the representation of landmarks. However, this dual-system hypothesis is based on paradigms that presented spatial cues conveying either conflicting or ambiguous spatial information and that amalgamated the concept of landmark into both discrete 3D objects and wall features. These confounded designs introduce difficulties in interpreting the spatial learning process. Here, we test the hypothesis of a complex interaction between the hippocampus and the striatum during landmark and geometry visual coding in humans. We also postulate that object-based and feature-based navigation are not equivalent instances of landmark-based navigation as currently considered in human spatial cognition. We examined the neural networks associated with geometry-, object-, and feature-based spatial navigation in an unbiased, two-choice behavioral paradigm using fMRI. We showed evidence of a synergistic interaction between hippocampal and striatal coding underlying flexible navigation behavior. The hippocampus was involved in all three types of cue-based navigation, whereas the striatum was more strongly recruited in the presence of geometric cues than object or feature cues. We also found that unique, specific neural signatures were associated with each spatial cue. Critically, object-based navigation elicited a widespread pattern of activity in temporal and occipital regions relative to feature-based navigation. These findings challenge and extend the current view of a dual, juxtaposed hippocampal-striatal system for visual spatial coding in humans. They also provide novel insights into the neural networks mediating object vs. feature spatial coding, suggesting a need to distinguish these two types of landmarks in the context of human navigation.HighlightsComplex hippocampal-striatal interaction during visual spatial coding for flexible human navigation behavior.Distinct neural signatures associated with object-, feature-, and geometry-based navigation.Object- and feature-based navigation are not equivalent instances of landmark-based navigation.

scholarly journals Role of Inferior Frontal Junction (IFJ) in the Control of Feature vs Spatial Attention

2020 ◽  
Author(s):  
Sreenivasan Meyyappan ◽  
Abhijit Rajan ◽  
George R Mangun ◽  
Mingzhou Ding
Keyword(s):  
Spatial Attention ◽  
Attention Control ◽  
Spatial Cues ◽  
Visual Spatial ◽  
Feature Based ◽  
Left And Right ◽  
Feature Attention ◽  
The Right ◽  
Inferior Frontal Junction

ABSTRACTFeature-based attention refers to preferential selection and processing of items and objects based on their non-spatial attributes such as color or shape. While it is intuitively an easier form of attention to relate to in our day to day lives, the neural mechanisms of feature-based attention are not well understood. Studies have long implicated the dorsal attention network as a key control system for voluntary spatial, feature and object-based attention. Recent studies have expanded on this model by focusing on the inferior frontal junction (IFJ), a region in the pre-frontal cortex to be the source of feature attention control, but not spatial attention control. However, the extent to which IFJ contributes to spatial attention remains a topic of debate. We investigated the role of IFJ in the control of feature versus spatial attention in a cued visual spatial (attend left or right) and feature attention (attend red or green) task using fMRI. Analyzing single-trial cue-evoked fMRI responses using univariate GLM and multi-voxel pattern analysis (MVPA), we observed the following. First, the univariate BOLD activation responses yielded no significant differences between feature and spatial cues. Second, MVPA analysis showed above chance level decoding in classifying feature attention (attend-red vs. attend-green) in both the left and right IFJ, whereas during spatial attention (attend-left vs. attend-right) decoding was at chance. Third, while the cue-evoked decoding accuracy was significant for both left and right IFJ during feature attention, target stimulus-evoked neural responses were not different. Importantly, only the connectivity patterns from the right IFJ was predictive of target-evoked activity in visual cortex (V4); this was true for both left and right V4. Finally, the strength of this connectivity between right IFJ and V4 (bilaterally) was found to be predictive of behavioral performance. These results support a model where the right IFJ plays a crucial role in top down control of feature but not spatial attention.

scholarly journals Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers

2019 ◽  
Vol 11 (4) ◽  
pp. 86 ◽  
Author(s):  
César Pérez López ◽  
María Delgado Rodríguez ◽  
Sonia de Lucas Santos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Income Tax ◽  
Fraud Detection ◽  
Personal Income ◽  
Personal Income Tax ◽  
Tax Returns ◽  
The Neural Networks ◽  
Tax Fraud ◽  
Efficiency Rate

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud.

Neural networks trained with high-dimensional functions approximation data in high-dimensional space

2021 ◽  
pp. 1-12
Author(s):  
Jian Zheng ◽  
Jianfeng Wang ◽  
Yanping Chen ◽  
Shuping Chen ◽  
Jingjin Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Dimensional Space ◽  
Data Distribution ◽  
High Dimensional ◽  
Sufficient Information ◽  
Sufficient Data ◽  
High Dimensional Space ◽  
Positive Effects ◽  
The Neural Networks ◽  
Using Data

Neural networks can approximate data because of owning many compact non-linear layers. In high-dimensional space, due to the curse of dimensionality, data distribution becomes sparse, causing that it is difficulty to provide sufficient information. Hence, the task becomes even harder if neural networks approximate data in high-dimensional space. To address this issue, according to the Lipschitz condition, the two deviations, i.e., the deviation of the neural networks trained using high-dimensional functions, and the deviation of high-dimensional functions approximation data, are derived. This purpose of doing this is to improve the ability of approximation high-dimensional space using neural networks. Experimental results show that the neural networks trained using high-dimensional functions outperforms that of using data in the capability of approximation data in high-dimensional space. We find that the neural networks trained using high-dimensional functions more suitable for high-dimensional space than that of using data, so that there is no need to retain sufficient data for neural networks training. Our findings suggests that in high-dimensional space, by tuning hidden layers of neural networks, this is hard to have substantial positive effects on improving precision of approximation data.

Classification and Recognition of Plant Leaf Based on Neural Networks

2011 ◽  
Vol 464 ◽  
pp. 38-42 ◽  
Author(s):  
Ping Ye ◽  
Gui Rong Weng
Keyword(s):  
Neural Networks ◽  
Fractal Dimension ◽  
Recognition Rate ◽  
Image Binarization ◽  
Characteristic Parameters ◽  
Moment Invariant ◽  
Plant Leaf ◽  
The Neural Networks ◽  
Novel Method ◽  
The Moment

This paper proposed a novel method for leaf classification and recognition. In the method, the moment invariant and fractal dimension were regarded as the characteristic parameters of the plant leaf. In order to extract the representative characteristic parameters, pretreatment of the leaf images, including RGB-gray converting, image binarization and leafstalk removing. The extracted leaf characteristic parameters were further utilized as training sets to train the neural networks. The proposed method was proved effectively to reach a recognition rate about 92% for most of the testing leaf samples

scholarly journals Localization of Scattering Objects Using Neural Networks

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 11
Author(s):  
Domonkos Haffner ◽  
Ferenc Izsák
Keyword(s):  
Neural Networks ◽  
Plane Waves ◽  
Measurement Data ◽  
Scattered Wave ◽  
Training Data ◽  
Data Set ◽  
Main Compound ◽  
Incident Plane ◽  
The Neural Networks ◽  
Simulation Package

The localization of multiple scattering objects is performed while using scattered waves. An up-to-date approach: neural networks are used to estimate the corresponding locations. In the scattering phenomenon under investigation, we assume known incident plane waves, fully reflecting balls with known diameters and measurement data of the scattered wave on one fixed segment. The training data are constructed while using the simulation package μ-diff in Matlab. The structure of the neural networks, which are widely used for similar purposes, is further developed. A complex locally connected layer is the main compound of the proposed setup. With this and an appropriate preprocessing of the training data set, the number of parameters can be kept at a relatively low level. As a result, using a relatively large training data set, the unknown locations of the objects can be estimated effectively.

scholarly journals An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array

2021 ◽  
Vol 13 (11) ◽  
pp. 6194
Author(s):  
Selma Tchoketch_Kebir ◽  
Nawal Cheggaga ◽  
Adrian Ilinca ◽  
Sabri Boulouma
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Modeling Process ◽  
Photovoltaic Arrays ◽  
Mode Of Operation ◽  
Different Types ◽  
The Neural Networks ◽  
Operational Modes ◽  
Types Of Faults ◽  
Electrical Data

This paper presents an efficient neural network-based method for fault diagnosis in photovoltaic arrays. The proposed method was elaborated on three main steps: the data-feeding step, the fault-modeling step, and the decision step. The first step consists of feeding the real meteorological and electrical data to the neural networks, namely solar irradiance, panel temperature, photovoltaic-current, and photovoltaic-voltage. The second step consists of modeling a healthy mode of operation and five additional faulty operational modes; the modeling process is carried out using two networks of artificial neural networks. From this step, six classes are obtained, where each class corresponds to a predefined model, namely, the faultless scenario and five faulty scenarios. The third step involves the diagnosis decision about the system’s state. Based on the results from the above step, two probabilistic neural networks will classify each generated data according to the six classes. The obtained results show that the developed method can effectively detect different types of faults and classify them. Besides, this method still achieves high performances even in the presence of noises. It provides a diagnosis even in the presence of data injected at reduced real-time, which proves its robustness.

scholarly journals Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lijun Song ◽  
Zhongxing Duan ◽  
Bo He ◽  
Zhe Li
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Estimation Error ◽  
Inertial Navigation ◽  
Transfer Alignment ◽  
The Neural Networks ◽  
Initial Attitude ◽  
Noise Statistics

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

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