Development Of Brain Science Research Articles

Brain-Inspired Learning, Perception, and Cognition: A Comprehensive Review.

The progress of brain cognition and learning mechanisms has provided new inspiration for the next generation of artificial intelligence (AI) and provided the biological basis for the establishment of new models and methods. Brain science can effectively improve the intelligence of existing models and systems. Compared with other reviews, this article provides a comprehensive review of brain-inspired deep learning algorithms for learning, perception, and cognition from microscopic, mesoscopic, macroscopic, and super-macroscopic perspectives. First, this article introduces the brain cognition mechanism. Then, it summarizes the existing studies on brain-inspired learning and modeling from the perspectives of neural structure, cognitive module, learning mechanism, and behavioral characteristics. Next, this article introduces the potential learning directions of brain-inspired learning from four aspects: perception, cognition, understanding, and decision-making. Finally, the top-ten open problems that brain-inspired learning, perception, and cognition currently face are summarized, and the next generation of AI technology has been prospected. This work intends to provide a quick overview of the research on brain-inspired AI algorithms and to motivate future research by illuminating the latest developments in brain science.

A review: Music-emotion recognition and analysis based on EEG signals.

Music plays an essential role in human life and can act as an expression to evoke human emotions. The diversity of music makes the listener's experience of music appear diverse. Different music can induce various emotions, and the same theme can also generate other feelings related to the listener's current psychological state. Music emotion recognition (MER) has recently attracted widespread attention in academics and industry. With the development of brain science, MER has been widely used in different fields, e.g., recommendation systems, automatic music composing, psychotherapy, and music visualization. Especially with the rapid development of artificial intelligence, deep learning-based music emotion recognition is gradually becoming mainstream. Besides, electroencephalography (EEG) enables external devices to sense neurophysiological signals in the brain without surgery. This non-invasive brain-computer signal has been used to explore emotions. This paper surveys EEG music emotional analysis, involving the analysis process focused on the music emotion analysis method, e.g., data processing, emotion model, and feature extraction. Then, challenging problems and development trends of EEG-based music emotion recognition is proposed. Finally, the whole paper is summarized.

What's All This Furor About an Enchanted Loom?

This article explores the gowing interconnections between the brain sciences and the social sciences, It porvides a brief historical summary of the development of brain science, reviews advances in what is currently known about the brain, and dfdescribes where the field stands today. Importantly for those interested in the social sciences, the article also discusses the potential impact of the brain sciences on work in the discipline, indicates why we should care about developments in the brain science field, and provides some practical tools that have come out of the resrarxh, The article concludes with a summary of what the developments might mean for a social sciences practitioner.

EEG Signal and Feature Interaction Modeling-Based Eye Behavior Prediction Research.

In recent years, with the development of brain science and biomedical engineering, as well as the rapid development of electroencephalogram (EEG) signal analysis methods, using EEG signals to monitor human health has become a very popular research field. The innovation of this paper is to analyze the EEG signal for the first time by building a depth factorization machine model, so that on the basis of analyzing the characteristics of user interaction, we can use EEG data to predict the binomial state of eyes (open eyes and closed eyes). The significance of the research is that we can diagnose the fatigue and the health of the human body by detecting the state of eyes for a long time. On the basis of this inference, the proposed method can make a further useful auxiliary support for improving the accuracy of the recommendation system recommendation results. In this paper, we first extract the features of EEG data by wavelet transform technology and then build a depth factorization machine model (FM+LSTM) which combines factorization machine (FM) and Long Short-Term Memory (LSTM) in parallel. Through the test of real data set, the proposed model gets more efficient prediction results than other classifier models. In addition, the model proposed in this paper is suitable not only for the determination of eye features but also for the acquisition of interactive features (user fatigue) in the recommendation system. The conclusion obtained in this paper will be an important factor in the determination of user preferences in the recommendation system, which will be used in the analysis of interactive features by the graph neural network in the future work.

Multi-modal human brain mapping and Brainnetome study

<p indent=0mm>With the development of neuroimaging and computing technologies, brain research based on <italic>in vivo</italic> imaging attracts mounting interests from multidisciplinary scientists with a variety of backgrounds, including neuroscience, clinical medicine, psychology, and engineering. Recently, cross-discipline collaborative research has formed new research directions and fields, and by contrasting to other research fields, this is a brilliant and unique feature in the development of brain science. Many imaging techniques for characterizing anatomical structures and detecting functional information in the brain have been populated in both basic neuroscience and clinical applications, including magnetic resonance imaging (MRI), electroencephalograph (EEG), magnetoencephalography (MEG). Meanwhile, brain science research puts higher requirements on advanced imaging techniques with higher temporal-spatial resolutions. Consequently, the data produced poses more challenges in data mining and modeling in deciphering the brain. To better describe the strategy of brain research from multiple spatiotemporal scales, the concept of Brainnetome was proposed. This paper systematically reviewed the current representative techniques of imaging-based brain research and emphasized the applications of multi-modal technologies in translational medicine, which is the research background and connotation of Brainnetome. Finally, we made a prospect for the future development of Brainnetome research.

Research status of anesthesia, neural circuits and artificial intelligence

The integration of science and brain-inspired technology has promoted the development of health industry, among which more and more researches about application of using integrate artificial in perioperative period and intensive care were performed. The development of brain science and brain-inspired intelligence will promote the development of anesthesiology and accelerate the process of integrating artificial into the perioperative period. The purpose of this review is to elucidate the mechanism of the neural circuits of anesthesia and the current status of the application of artificial in clinical anesthesia and intensive care. This review focuses on the role of anesthetic neural circuits in analgesia, sedation and awakening, and the research advances of application of clinical decision support system based on machine learning in perioperative monitoring and prediction of postoperative complications. Key words: Anesthesia; Neural circuits; Artificial intelligence; Big database; Clinical decision support

Can psychiatry and neurology 'simply' merge?

I appreciate Professor Fitzgerald's citation of my 2005 article, titled ‘Why psychiatry and neurology cannot simply merge’,1,2 however, he seems to have misconstrued the essential nature of my argument. He positions his discussion of my article just after the statement, ‘The chorus of disapproval against neuropsychiatry has certainly grown’. But I would like to assure Professor Fitzgerald that I am not, nor have I ever been, part of such a ‘chorus’. A careful reading of my article will show that the key word in my argument is ‘simply’. I am not opposed in any way to integrating neurology and psychiatry; rather, I argue that certain types of ‘bridging’ concepts and constructs would be necessary to bring about such a union. I describe neuropsychiatry as ‘a vitally important transitional stage in the development of brain science’. Indeed, I would argue that neuropsychiatry is the crucible within which the discourses of psychiatry and neurology will eventually ‘bond’, producing a narrative that incorporates the dialectical and subtextual understanding of psychiatry into the framework of neurophysiology and neuropathology. But until such a meta-narrative has evolved, there cannot be a genuine merger of psychiatry and neurology. Or rather, we should say that without such a meta-narrative, the nature of the merger would be more like the grafting of an oak branch onto a maple tree than the hybridisation of two varieties of rose.2 I fully agree with Professor Fitzgerald that ‘the separation of neurology from psychiatry has led to a separation of the brain from the mind – the physical from the mental – which has been unhelpful for both disciplines’. That said, I do not accept the view that psychiatric disease is best described as ‘brain disease’ or that mental constructs are ‘reducible’ to mere physiological or neuroanatomical terms. But this is a complicated philosophical issue best left for a longer communication.3 Stated briefly, I believe that ‘disease’ is most usefully predicated of persons, not minds or brains, and that there are ways in which a union of neurology and psychiatry could contribute to a very rich understanding of the human person, and how personhood is undermined and compromised by disease states like schizophrenia.4

Constructing a New-Style Conceptual Model of Brain Data for Systematic Brain Informatics

The development of brain science has led to a vast increase of brain data. To meet requirements of a systematic methodology of Brain Informatics (BI), this paper proposes a new conceptual model of brain data, namely Data-Brain, which explicitly represents various relationships among multiple human brain data sources, with respect to all major aspects and capabilities of human information processing systems (HIPS). A multidimension framework and a BI methodology-based ontological modeling approach have been developed to implement a Data-Brain. The Data-Brain, Data-Brain-based BI provenances, and heterogeneous brain data can be used to construct a Data-Brain-based brain data center which provides a global framework to integrate data, information, and knowledge coming from the whole research process for systematic BI study. Such a Data-Brain modeling approach represents a radically new way for domain-driven conceptual modeling of brain data, which models a whole process of systematically investigating human information processing mechanisms.

Random-access Two-photon Microscopy for Neural Activity Observation*

Two-photon microscope has become an important instrument in neuroscience research.However,the current commercial instruments can hardly meet the need for the detection of neural signal in millisecond scale due to their low imaging rates.Fast random-access two-photon microscopy based on acousto-optic deflector(AOD) has the potential for increasing the observation speed while maintaining adequate signal to noise ratio(SNR).We summarize the latest related research progress.It is demonstrated from four parts,including the spatio-temporal evolution theory of the femtosecond laser after passing the angular dispersion devices,dispersion compensation method for AOD,random-access two-photon microscopy instrument,and calcium signal identification method in the instrument applications.In the end,the future development trends for random-access two-photon microscopy are discussed.The systematic and in-deep research on this technology will provide a new tool for the neural activity observation and boost the development of brain science.

Quantitative Aspects to Care by HR Professionals

The 2015 Future of Human Resource Management report identifies major forces that will influence the workplace and as a result the HR profession in the years ahead. These forces, a combination of both trends and uncertainties, were used to develop four different scenarios of what the working world could look like in 2015. These worlds offer HR professionals a view into the future and an opportunity to explore what roles they may play. They also offer further context to identify the skills and competencies that will be needed across the organization and within the HR profession itself. If we see beyond 2015 then we have to consider the development of brain science which would lead the behavioral science thus the HRM and Human Resource Development. The days are also ahead when HRD and HRM must think about even Nano Technology. Hence, HR should know beyond the today’s HRM but the tomorrows behavioural science and quantitative to add vale to organization.

Is There a Need for Clinical Neuroskepticism?

Clinical neuroethics and neuroskepticism are recent entrants to the vocabulary of neuroethics. Clinical neuroethics has been used to distinguish problems of clinical relevance arising from developments in brain science from problems arising in neuroscience research proper. Neuroskepticism has been proposed as a counterweight to claims about the value and likely implications of developments in neuroscience. These two emergent streams of thought intersect within the practice of neurology. Neurologists face many traditional problems in bioethics, like end of life care in the persistent vegetative state, determination of capacity in progressive dementia, and requests for assisted suicide in cognition-preserving neurodegenerative disease (like amyotrophic lateral sclerosis). Neurologists also look to be at the forefront of downstream clinical applications of neuroscience, like pharmacological enhancement of mental life. At the same time, the practice of neurology, concerned primarily with the structure, function, and treatment of the nervous system, has historically fostered a kind of skeptical attitude toward its own subject matter. Not all problems that appear primarily neurological are primarily neurological. This disciplinary skepticism is generally clinical in orientation and limited in scope. The rise of interest in clinical neuroethics and in neuroskepticsim generally suggests a possible broader application. The clinical skepticism of neurology provides impetus for thinking about the appropriate role for skepticism in clinical areas of neuroethics. After a brief review of neuroskepticism and clinical neuroethics, a taxonomy of clinical neuroskepticism is offered and reasons why a stronger rather than weaker form of clinical neuroskepticism is currently warranted.

Revelations of the New Developments of Brain Science on Early Education

The development of information technology has drove the large revolution of brain science and made it go into the new development stage. The new development of brain science must deeply influence the education, especially for the early education, which makes our attention focus on how to develop children brain again. From the world developments of the research of “brain science and education”, in this article, we will research the revelations of the concepts such as “learning” and “education” in the domain of the cognitive neuroscience and the development of brain science on the early education.

Acupuncture in treatment of cerebrosis: advances in clinical research over 20 years

Over the past twenty years, with the development of brain science, many acupuncture clinical researchers have used multidisciplinary methods and techniques to perform acupuncture clinical research on cerebrosis. Up to now, many achievements have been obtained such as expanding studied disease entities, standardizing methods, identifying objects, and developing more and more treatment methods and in-depth mechanism research.

Gene Maps, Brain Scans, and Psychiatric Nosology

Neuroethics to date has tended to focus on social and ethical implications of developments in brain science, especially in functional neuroimaging. Within clinical neuroethics , the emphasis has been on ethical issues in clinical neuroscience practice, including informed consent to neuroimaging; the development of ethical research protocols for functional magnetic resonance imaging especially, and especially in children; and the ethical clinical management of incidental findings. Within normative neuroethics , we have witnessed the more philosophical and/or social scientific study of the meanings of developments in neuroscience, including concerns about the impact of neuroimaging on privacy, freedom of thought, moral culpability, and sense of self. In this piece, I argue for an expansion of neuroethical attention to the interface of neuroscience and psychiatry, where brain science meets the clinical sciences of the mind. My particular focus is the development of psychiatric classification systems. I am grateful to Jenny Brian, Carl Craver, Thane Plantikow, Claire Pouncey, and Ken Schaffner for discussion of many of the themes presented here. Early research on which portions of this article are based was funded by the Canadian Institutes of Health Research in the form of an operating grant and salary award. My current research is supported by the Institute for Humanities Research, the Center for Biology and Society, and the School of Life Sciences, Arizona State University.

Three-dimensional reconstruction of functional brain images

We consider PET (positron emission tomography) measurement with SPM (Statistical Parametric Mapping) analysis to be one of the most useful methods to identify activated areas of the brain involved in language processing. SPM is an effective analytical method that detects markedly activated areas over the whole brain. However, with the conventional presentations of these functional brain images, such as horizontal slices, three directional projection, or brain surface coloring, makes understanding and interpreting the positional relationships among various brain areas difficult. Therefore, we developed three-dimensionally reconstructed images from these functional brain images to improve the interpretation. The subjects were 12 normal volunteers. The following three types of images were constructed: 1) routine images by SPM, 2) three-dimensional static images, and 3) three-dimensional dynamic images, after PET images were analyzed by SPM during daily dialog listening. The creation of images of both the three-dimensional static and dynamic types employed the volume rendering method by VTK (The Visualization Toolkit). Since the functional brain images did not include original brain images, we synthesized SPM and MRI brain images by self-made C++ programs. The three-dimensional dynamic images were made by sequencing static images with available software. Images of both the three-dimensional static and dynamic types were processed by a personal computer system. Our newly created images showed clearer positional relationships among activated brain areas compared to the conventional method. To date, functional brain images have been employed in fields such as neurology or neurosurgery, however, these images may be useful even in the field of otorhinolaryngology, to assess hearing and speech. Exact three-dimensional images based on functional brain images are important for exact and intuitive interpretation, and may lead to new developments in brain science. Currently, the surface model is the most common method of three-dimensional display. However, the volume rendering method may be more effective for imaging regions such as the brain.

Psychobiological Politics

AbstractStrikingly innovative developments in brain science during the past two decades, reflecting advances in a dozen different biological disciplines (such as biochemistry, biophysics, endocrinology, neuropsychology, genetics, and human development) have created a new psychobiology that thus far appears to have had only slight impact upon mainstream political science theory and research. This field analysis examines the implications of psychobiology for the study and practice of politics, from the perspective of the founding father of political behaviouralism. The article discusses the psychobiology of mind in terms of human consciousness and memory and then examines the epigenetic and recursive relationships between brain structure and political perception; between brain lateralization and dynamics, and political thinking and decision-making; and between brain development and political equality, with particular regard to sex, age, health, race, and intelligence.