Learning In Human Brain Research Articles

Multi-Channel Decomposition in Tandem With Free-Energy Principle for Reduced-Reference Image Quality Assessment

The visual quality of perceptions is highly correlated with the mechanisms of the human brain and visual system. Recently, the free-energy principle, which has been widely researched in brain theory and neuroscience, is introduced to quantize the perception, action, and learning in human brain. In the field of image quality assessment (IQA), on one hand, the free-energy principle can resort to the internal generative model to simulate the visual stimulus of the human beings. On the other hand, abundant psychological and neurobiological studies reveal that different frequency and orientation components of one visual stimulus arouse different neurons in the striate cortex, and the striate cortex processes visual information in the cerebral cortex. Motivated by these two aspects, a novel reduce-reference IQA metric called the multi-channel free-energy based reduced-reference quality metric is proposed in this paper. First, a two-level discrete Haar wavelet transform is used to decompose the input reference and distorted images. Next, to simulate the generative model in the human brain, the sparse representation is leveraged to extract the free-energy-based features in subband images. Finally, the overall quality metric is obtained through the support vector regressor. Extensive experimental comparisons on four benchmark image quality databases (LIVE, CSIQ, TID2008, and TID2013) demonstrate that the proposed method is highly competitive with the representative reduced-reference and classical full-reference models.

Maternal odor shapes rapid face categorization in the infant brain.

To successfully interact with a rich and ambiguous visual environment, the human brain learns to differentiate visual stimuli and to produce the same response to subsets of these stimuli despite their physical difference. Although this visual categorization function is traditionally investigated from a unisensory perspective, its early development is inherently constrained by multisensory inputs. In particular, an early-maturing sensory system such as olfaction is ideally suited to support the immature visual system in infancy by providing stability and familiarity to a rapidly changing visual environment. Here, we test the hypothesis that rapid visual categorization of salient visual signals for the young infant brain, human faces, is shaped by another highly relevant human-related input from the olfactory system, the mother's body odor. We observe that a right-hemispheric neural signature of single-glance face categorization from natural images is significantly enhanced in the maternal versus a control odor context in individual 4-month-old infant brains. A lack of difference between odor conditions for the common brain response elicited by both face and non-face images rules out a mere enhancement of arousal or visual attention in the maternal odor context. These observations show that face-selective neural activity in infancy is mediated by the presence of a (maternal) body odor, providing strong support for multisensory inputs driving category acquisition in the developing human brain and having important implications for our understanding of human perceptual development.

Reduce False Positive Alerts for Elderly Person Fall Video-Detection Algorithm by convolutional neural network model

Currently, image acquisition and understanding has become a necessity. In fact, it’s what allows machines to become one of the most powerful tools.Nowadays, machines that replace humans and experts in making decisions in several areas have seen their success from what so-called deep learning, a powerful learning in Machine learning tool for processing, classifying and object recognition tasks. The idea behind deep learning is training machines, adapting their skills and applying them to many tasks. In the same way that human brain learns, the information is entered through our senses (eyes...) and goes through billions of neurons before it was processed to get an output, deep learning also takes information as in input then start proceeding through several hidden layers before an output layer.For that, we choose to profit from this powerful learning to improve the Video fall detection algorithm which suffers from generating a huge amount of false alarms. So we propose in our work to minimize these false alerts using a CNN model that can classify a person sitting in a wheelchair from others to eliminate them.We present in this paper, on the one hand, a survey of the most recent and powerful architectures of CNN, on the other, we propose to add a CNN model into the elderly person fall Video-Detection Algorithm to improve its accuracy.

Law, discipline and governance based on cognitive process simulation – analysis of legal forms in the modern rights relationship

People are becoming more and more humanized in the process of understanding the law. According to the right to discipline, the law has its own core setting factors, while some limits can't reach people's desire. Therefore, the legal and illegal mode of transcending rights is very important. In order to analyze the legal form of modern rights, in this paper, the cognitive learning and memory process of human brain were simulated through the artificial neural network and the understanding of human brain structure, and the role of law, discipline and governance was reflected. In the study, the structure and algorithm of the model neural network were optimized, the memory forgetting curve mechanism that can simulate the human brain was introduced, and thus the network recognition rate was improved. And in the algorithm, the calculation of matching degree was avoided, and the computational complexity was reduced to the sample. Then the sample was compared with the SOM, ART1, and PNN algorithms. The experimental simulation results show that the recognition speed of this sample is 1.9 times faster than that of ART1, 58 times than that of SOM, and 1.5 times than that of the PNN network.

Psychophysical Response for Intensity Variation in Pure Tone Sound - Attention & Retention Assessment in Human Beings

Background/Objectives: Human brain learns from conditioning. Also learning is an inherent activity exhibited by every human being. A better training/conditioning leads to a better learning and in remembering as well. Methods/Statistical Analysis: The present work focuses on the assessment of behavioural response for a predefined set of single tone sound with varying intensity after being conditioned for 20 days and after a break period of 7 days and assessing the same on day 28 for the retention based behavioural response. A pure tone complex of 1 KHz, 500 milliseconds duration was used and the test was conducted in the morning and evening in a noise free environment. A total of 44 subjects, both males and females of 20–30 years of no known auditory pathological history were considered. Findings: The results showed a distinct betterment of the auditory perception over the training period hinting at the attention abilities of the subjects. After a break of seven days, substantial information gained during the training phase was found to be retained thereby proving the retention abilities of the individuals. The presently obtained results provide a cue towards the learning abilities of human beings with gradual training and also to the extent to which the human beings can retain the aspects learnt during the training sessions. The results also demonstrated that the males have a better attention as well as retention abilities than females. Application/Improvements: The presently designed protocol is successfully able to assess the attention and retention based abilities of the individuals. This method can be extended to assess the auditory temporal resolution of professionals in case of basket ball and table tennis sports and also to assess the deterioration of the hearing perception of traffic police and the workers of fabrication and chemical industries.

How Is Building Lego Models Related to Math Skills?

Math is usually taught using a lot of words. But, is this the way the human brain learns math? We studied how math is related to memory, intelligence, and reading in 7-year-old children. We found that memory for visual information (things you can see) and spatial information (where things are located in relationship to each other) is related to math skills more than memory for words and verbal information. Interestingly, previous studies have found that building Lego models (construction play) by following instructions is related to math skills. This study found that the relationship between construction play and math is influenced by memory for visual and spatial information. The results of this study make us wonder if building Lego models could improve the brain processes needed for learning math!

Unconstrained face detection: a Deep learning and Machine learning combined approach

In uncontrolled environment the faces present multiple challenges. The primary challenges are occlusion, pose and illumination variation. In such cases it becomes difficult to detect faces for further processing. Convolutional Neural Network (CNN) is a bio-inspired network that learns the way human brain learns. CNN offers deep observation of features present in input image. This paper presents the combined approach to detect faces using deep features extracted by deep CNN and the classification by Cubic Support vector Machine. Area based approach is used for removal of distant faces and background pixels to reduce the processing time required per frame at detection stage.

Dynamical Representation of Dominance Relationships in the Human Rostromedial Prefrontal Cortex

Humans and other primates have evolved the ability to represent their status in the group's social hierarchy, which is essential for avoiding harm and accessing resources. Yet it remains unclear how the human brain learns dominance status and adjusts behavior accordingly during dynamic social interactions. Here we address this issue with a combination of fMRI and transcranial direct current stimulation (tDCS). In a first fMRI experiment, participants learned an implicit dominance hierarchy while playing a competitive game against three opponents of different skills. Neural activity in the rostromedial PFC (rmPFC) dynamically tracked and updated the dominance status of the opponents, whereas the ventromedial PFC and ventral striatum reacted specifically to competitive victories and defeats. In a second experiment, we applied anodal tDCS over the rmPFC to enhance neural excitability while subjects performed a similar competitive task. The stimulation enhanced the relative weight of victories over defeats in learning social dominance relationships and exacerbated the influence of one's own dominance over competitive strategies. Importantly, these tDCS effects were specific to trials in which subjects learned about dominance relationships, as they were not present for control choices associated with monetary incentives but no competitive feedback. Taken together, our findings elucidate the role of rmPFC computations in dominance learning and unravel a fundamental mechanism that governs the emergence and maintenance of social dominance relationships in humans.

A novel control strategy for DVR: Optimal bi-objective structure emotional learning

There are different approaches to provide high power quality for sensitive loads in fault conditions. In this research, “Dynamic Voltage Restorer (DVR)” is used to compensate the harmful effects of disturbances on voltage. In order to improve the performance of DVR’s controller from point of view of power quality indices, voltage sag and THD are considered as first and second object respectively so a new structure is suggested for this controller. The proposed controller is based on human brain learning which is a self-tuning PI controller that is called emotional controller. In that paper a bi-objective structure emotional learning is recommended. Also using this controller, system had better performance during fault conditions in term of both these power quality’s indices. Furthermore, considering voltage THD as second goal influence on major goal considerably which is voltage sag. Power systems sometimes have complicated dynamic behavior especially during faults. Most controllers have difficulty doing their best performance in these situations. Therefore, in order to modify the performance of this controller from point of view of mentioned power quality indices, we make a decision to regulate this controller’s parameters with an optimization algorithm. Self Adaptive Modified Bat Algorithm (SAMBA) is considered as a powerful optimization algorithm in this paper. This optimization algorithm is a modified version of Bat algorithm. The optimization algorithm can overcome to some problems which are common in other optimization algorithms. The algorithm has had a considerable performance rather than standard Bat and PSO algorithm. Also according to simulation results, this proposed method works significantly better than classic PI controller, bi-objective emotional controller and some intelligent controllers that have introduced in other researches already.

A novel self-tuning control method based on regulated bi-objective emotional learning controller's structure with TLBO algorithm to control DVR compensator

DVR is one of the custom power devices for compensating power quality indices. A self-tuning controller with a bi-objective structure is presented for controlling the DVR compensator in order to improve the THD and voltage sag indices of a sensitive load in the network. In this paper, the emotional controller which is based on emotional learning of human brain is proposed for controlling the DVR compensator. This controller has such a structure that makes it capable of considering a second objective in the control process of the system. So far, this capability of the emotional controller has not been used in any researches. The results of the paper demonstrate that compensating and controlling the voltage THD signal in the control process has caused more improvement in the voltage sag of the sensitive load. It was reported that the performance of the emotional controller depends on the selection of the values of its coefficients. Therefore, in order to better improve the proposed controller, these coefficients are tuned by an optimization algorithm. Teaching–learning-based optimization algorithm is considered as optimization algorithm to regulate these coefficients. According to simulation results, it works significantly better than classic PI controller and some intelligent controllers that have introduced in other researches already.

The Efficacy of Mathematics Education

Evidence supports the notion that mathematics education in the United States is inadequate. There is also evidence that mathematics education deficiencies extend internationally. The worldwide mathematics education deficit appears large enough that improving student performance in this educational problem area could yield great economic benefit. To improve the efficacy of mathematics education, education’s root problems must first be understood. Often supposed educational root problems are considered and contrasted against potential deficiencies of mathematics methodologies and curricula that are based on mainstream educational philosophies. The educational philosophies utilized to form early-grade mathematics methodologies and related curricula are judged to be the main reasons for low levels of interest in the subject of mathematics by student populations. An exploration of available literature in regard to how the human brain learns is provided. Two unifications are resultantly hypothesized: interest and learning appear to be mentally intertwined, this unification may serve as the basis for a more effective educational methodology; children are nearly universally interested in the visual arts, arithmetic and geometric mathematical principles are intertwined with the visual arts principles of linear perspective and proportion. An early grade curriculum that relates those mathematical principles via artistic methods (similar to those developed and utilized by master artists and engineers of the Renaissance era) may prove broadly effective at equitably increasing students’ mathematical achievement. Future research in such a direction is highly recommended.

Functional and structural correlates of face view perceptual learning in human brain

Functional and structural correlates of face view perceptual learning in human brain

Mapping the mind

Mapping the brain's exact micro-circuitry in order to get much clearer answers is an undertaking on a completely different scale, not least because the human brain has 100 billion neurons, each one linked to around 10,000 others. And yet such detail is becoming tantalisingly within reach as scientists in a number of major projects are beginning to make progress in understanding, modelling and simulating the brain down to nanometre resolutions. Scientists may be able to improve treatments for neurodegenerative conditions, and even make faster computers, by figuring out how the human brain learns and remembers.

Self and identity: a brief overview of what they are, what they do, and how they work

The human self exists at the interface between the animal body and the social system. Solitary beings would hardly need or have selves, but social and cultural systems define identities, and the human animal acquires selfhood in order to function in these systems. Self begins with the physical body, with acting and choosing as a unity, and as a point of reference distinct from others, and it acquires meaningful content by participating in the social system. The self is not contained in the brain, but rather the human brain learns to operate a self.

Music and the Brain, Literally

Music and the Brain, Literally

A study on striatum involved in associative learning memory by functional magnetic resonance imaging technique and clinical observation

Objective To examine whether the striatum are involved in associative learning and memory cognitive function of human brain by functional magnetic resonance imaging (fMRI) technique and clinical observation.Methods 14 right-handed normal volunteers participated in a test of paired-word associative learning and memory ,while the fMRI data were recorded.Control tasks were performed for the block-design.SPM 99 was used to analyze the data and to obtain the activated brain regions.14 patients with striatum damages and 14 matched normal volunteers participated in a test of paired-word associative learning and memory.Results When the threshold was set as P＜0.005 ,using a one-sample t -test ,the left occipital lobe (t=13.87)were activated remarkably during the encoding process of the paired-word associative learning and memory task.However,during the retrieval process,the prominently activated brain regions were the left parietal lobe (t=8.73).The striatum (t=7.02/7.47) were also obviously activated during these two stages.The scores of the paired-word associative learning and memory test on the patients with striatum damages and the healthy volunteers were respectively 11.53±1.39 and 19.18±2.89 ,the difference was significant (P ＜ 0.001).Conclusion The results of this study reveal that the subcortical structures such as the striatum as well as the cerebral cortex are involved in the associative learning and memory in human brain ,and the damages of striatum would show the disorder of associative learning and memory cognitive function. Key words: Functional magnetic resonance imaging (fMRI) ; Associative learning and memory; Cognitive function; Striatum; Human brain

Association between NR2B subunit gene ( GRIN2B) promoter polymorphisms and sporadic Alzheimer’s disease in the North Chinese population

N-methyl- d-aspartate (NMDA) receptor plays a crucial role in learning, memory and information processing of human brain. Its dysfunction is related to the pathogenesis of Alzheimer’s disease (AD). We detected four polymorphisms of the promoter regions of the human NMDA receptor 2B (NR2B) subunit gene ( GRIN2B) in 362 AD patients and 334 healthy in North Han Chinese populations, these were −200T/G (rs1019385), −421C/A (rs3764028), −1447T/C (ENS10557853), and −1497G/A (rs12368476). Genetic analysis confirmed that there were significant differences in genotype ( P = 0.029) and allele ( P = 0.010) frequencies for −421C/A between SAD and control. In the subjects without APOE ɛ4 allele, these difference remained significant (genotype P = 0.012, allele P = 0.004). The −421CC genotype was about 1.5 fold increasing risk compared with CA + AA genotypes (OR = 1.517, 95% CI 1.077–2.137, P = 0.017). Luciferase reporter assay showed a 34.69–39.79% decrease in transcriptional activity for −421C allele of GRIN2B promoter compared with −421A in SH-SY5Y and HeLa cell lines. Our study suggests that the −421C allele may induce lower GRIN2B transcriptional activity and NR2B protein expression, thus it is associated with AD.

Distributed Neural Plasticity for Shape Learning in the Human Visual Cortex

Expertise in recognizing objects in cluttered scenes is a critical skill for our interactions in complex environments and is thought to develop with learning. However, the neural implementation of object learning across stages of visual analysis in the human brain remains largely unknown. Using combined psychophysics and functional magnetic resonance imaging (fMRI), we show a link between shape-specific learning in cluttered scenes and distributed neuronal plasticity in the human visual cortex. We report stronger fMRI responses for trained than untrained shapes across early and higher visual areas when observers learned to detect low-salience shapes in noisy backgrounds. However, training with high-salience pop-out targets resulted in lower fMRI responses for trained than untrained shapes in higher occipitotemporal areas. These findings suggest that learning of camouflaged shapes is mediated by increasing neural sensitivity across visual areas to bolster target segmentation and feature integration. In contrast, learning of prominent pop-out shapes is mediated by associations at higher occipitotemporal areas that support sparser coding of the critical features for target recognition. We propose that the human brain learns novel objects in complex scenes by reorganizing shape processing across visual areas, while taking advantage of natural image correlations that determine the distinctiveness of target shapes.

Purposeful Prediction of Space Weather Phenomena by Simulated Emotional Learning

Bounded rationality and satisfying models, rather than optimization techniques, have shown good performance in decision making. The emotional learning algorithm is an example. It is based on simulating human emotions via reinforcement agents. A new approach towards purposeful prediction problems, derived from a recently developed model of emotional learning in human brain, is introduced in this article. The proposed algorithm inherently emphasizes learning to predict future peaks, and performs remarkably accurate predictions among the important regions, features, or objectives. Space weather forecasting is an excellent example of using this methodology, and in fact was the motivation to introduce purposeful prediction via multiobjective learning algorithm in this research. Three examples of predicting solar activity, geomagnetic activity, and geomagnetic storms show the characteristics of the suggested algorithm and its usefulness to space weather warning and alert systems. The successful application of the proposed model indicates the significance of structural brain modelling beyond traditional neural networks, as well as the importance of biological motivation in choosing a suitable model for any given application.

Pile Driving Records Reanalyzed Using Neural Networks

Pile driving formulas are commonly used to estimate the load capacity of driven piles. The formulas assume that there is a correlation between the pile set and the ultimate load capacity of the pile. The important factors influencing the load capacity include the hammer characteristics, the properties of the pile and soil, and the pile set. The present technical note investigates the feasibility of using neural networks to predict the load capacity of driven piles. Neural networks attempt to simulate the process by which the human brain learns to discern patterns in arrays of data. The data used in this study were derived from actual pile driving records. First, the neural network concepts are reviewed, then the neural network model for predicting the pile capacity is presented. The neural network predictions were found to be more consistent and reliable than other, more conventional pile driving formulas.