scholarly journals Creating a Computable Cognitive Model of Visual Aesthetics for Automatic Aesthetics Evaluation of Robotic Dance Poses

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 23
Author(s):  
Hua Peng ◽  
Jing Li ◽  
Huosheng Hu ◽  
Keli Hu ◽  
Chao Tang ◽  
...  
Keyword(s):  
Human Brain ◽  
Visual Information ◽  
Intelligent System ◽  
Cognitive Model ◽  
Neural Models ◽  
Visual Aesthetics ◽  
Machine Learning Methods ◽  
Cognitive Computation ◽  
Machine Aesthetics ◽  
Human Brains

Inspired by human dancers who can evaluate the aesthetics of their own dance poses through mirror observation, this paper presents a corresponding mechanism for robots to improve their cognitive and autonomous abilities. Essentially, the proposed mechanism is a brain-like intelligent system that is symmetrical to the visual cognitive nervous system of the human brain. Specifically, a computable cognitive model of visual aesthetics is developed using the two important aesthetic cognitive neural models of the human brain, which is then applied in the automatic aesthetics evaluation of robotic dance poses. Three kinds of features (color, shape and orientation) are extracted in a manner similar to the visual feature elements extracted by human brains. After applying machine learning methods in different feature combinations, machine aesthetics models are built for automatic evaluation of robotic dance poses. The simulation results show that our approach can process visual information effectively by cognitive computation, and achieved a very good evaluation performance of automatic aesthetics.

scholarly journals Cellular resolution anatomical and molecular atlases for prenatal human brains

2021 ◽  
Author(s):  
Song-Lin Ding ◽  
Joshua J. Royall ◽  
Phil Lesnar ◽  
Benjamin A.C. Facer ◽  
Kimberly A. Smith ◽  
...  
Keyword(s):  
Human Brain ◽  
Hippocampal Formation ◽  
Marker Gene ◽  
Brain Structures ◽  
Marker Genes ◽  
Subcortical Structures ◽  
Cellular Resolution ◽  
Reliable Marker ◽  
Human Brains ◽  
Developmental Features

Increasing interest in studies of prenatal human brain development, particularly using new single-cell genomics and anatomical technologies to create cell atlases, creates a strong need for accurate and detailed anatomical reference atlases. In this study, we present two cellular-resolution digital anatomical atlases for prenatal human brain at post-conceptional weeks (PCW) 15 and 21. Both atlases were annotated on sequential Nissl-stained sections covering brain-wide structures on the basis of combined analysis of cytoarchitecture, acetylcholinesterase staining and an extensive marker gene expression dataset. This high information content dataset allowed reliable and accurate demarcation of developing cortical and subcortical structures and their subdivisions. Furthermore, using the anatomical atlases as a guide, spatial expression of 37 and 5 genes from the brains respectively at PCW 15 and 21 was annotated, illustrating reliable marker genes for many developing brain structures. Finally, the present study uncovered several novel developmental features, such as the lack of an outer subventricular zone in the hippocampal formation and entorhinal cortex, and the apparent extension of both cortical (excitatory) and subcortical (inhibitory) progenitors into the prenatal olfactory bulb. These comprehensive atlases provide useful tools for visualization, targeting, imaging and interpretation of brain structures of prenatal human brain, and for guiding and interpreting the next generation of cell census and connectome studies.

scholarly journals Informative neural representations of unseen objects during higher-order processing in human brains and deep artificial networks

2021 ◽  
Author(s):  
Ning Mei ◽  
Roberto Santana ◽  
David Soto
Keyword(s):  
Visual Processing ◽  
Visual Information ◽  
Model Simulation ◽  
Conscious Experience ◽  
Neural Representation ◽  
Visual Stream ◽  
Order Processing ◽  
High Level ◽  
Unseen Objects ◽  
Human Brains

AbstractDespite advances in the neuroscience of visual consciousness over the last decades, we still lack a framework for understanding the scope of unconscious processing and how it relates to conscious experience. Previous research observed brain signatures of unconscious contents in visual cortex, but these have not been identified in a reliable manner, with low trial numbers and signal detection theoretic constraints not allowing to decisively discard conscious perception. Critically, the extent to which unconscious content is represented in high-level processing stages along the ventral visual stream and linked prefrontal areas remains unknown. Using a within-subject, high-precision, highly-sampled fMRI approach, we show that unconscious contents, even those associated with null sensitivity, can be reliably decoded from multivoxel patterns that are highly distributed along the ventral visual pathway and also involving prefrontal substrates. Notably, the neural representation in these areas generalised across conscious and unconscious visual processing states, placing constraints on prior findings that fronto-parietal substrates support the representation of conscious contents and suggesting revisions to models of consciousness such as the neuronal global workspace. We then provide a computational model simulation of visual information processing/representation in the absence of perceptual sensitivity by using feedforward convolutional neural networks trained to perform a similar visual task to the human observers. The work provides a novel framework for pinpointing the neural representation of unconscious knowledge across different task domains.

A human brain test bed for research in large vessel occlusion stroke

2021 ◽  
pp. 1-9
Author(s):  
Daniel Gebrezgiabhier ◽  
Yang Liu ◽  
Adithya S. Reddy ◽  
Evan Davis ◽  
Yihao Zheng ◽  
...  
Keyword(s):  
Human Brain ◽  
Mechanical Thrombectomy ◽  
Posterior Circulation ◽  
Large Vessel ◽  
Suction Catheter ◽  
Large Vessel Occlusion ◽  
Test Bed ◽  
Anterior Circulation ◽  
Vessel Occlusion ◽  
Human Brains

OBJECTIVEEndovascular removal of emboli causing large vessel occlusion (LVO)–related stroke utilizing suction catheter and/or stent retriever technologies or thrombectomy is a new standard of care. Despite high recanalization rates, 40% of stroke patients still experience poor neurological outcomes as many cases cannot be fully reopened after the first attempt. The development of new endovascular technologies and techniques for mechanical thrombectomy requires more sophisticated testing platforms that overcome the limitations of phantom-based simulators. The authors investigated the use of a hybrid platform for LVO stroke constructed with cadaveric human brains.METHODSA test bed for embolic occlusion of cerebrovascular arteries and mechanical thrombectomy was developed with cadaveric human brains, a customized hydraulic system to generate physiological flow rate and pressure, and three types of embolus analogs (elastic, stiff, and fragment-prone) engineered to match mechanically and phenotypically the emboli causing LVO strokes. LVO cases were replicated in the anterior and posterior circulation, and thrombectomy was attempted using suction catheters and/or stent retrievers.RESULTSThe test bed allowed radiation-free visualization of thrombectomy for LVO stroke in real cerebrovascular anatomy and flow conditions by transmural visualization of the intraluminal elements and procedures. The authors were able to successfully replicate 105 LVO cases with 184 passes in 12 brains (51 LVO cases and 82 passes in the anterior circulation, and 54 LVO cases and 102 passes in the posterior circulation). Observed recanalization rates in this model were graded using a Recanalization in LVO (RELVO) scale analogous to other measures of recanalization outcomes in clinical use.CONCLUSIONSThe human brain platform introduced and validated here enables the analysis of artery-embolus-device interaction under physiological hemodynamic conditions within the unmodified complexity of the cerebral vasculature inside the human brain.

Simulation of Human Brain

2021 ◽  
pp. 150-160
Author(s):  
Navita Malik ◽  
Arun Solanki
Keyword(s):  
Human Brain ◽  
Computer Science ◽  
Learning Process ◽  
Intelligent System ◽  
Classroom Teaching ◽  
Reinforcement Material ◽  
Ict Tools ◽  
Mode Of Instruction ◽  
Teaching Learning

AI is a branch of computer science that gives the ability to a computer to think and make decisions like humans. It stimulates the human brain in the computer and makes appropriate decisions when required. AI-enabled education impacts the designing of curriculum, mode of instruction, and many more. The use of these tools revolutionizing the education sector with the progression of ICT tools have now become AI-enabled. The main feature of an AI-enabled tool is personalization. These AI-enabled tools work like intelligent assistants for the students. The intelligent system having features like answer the queries of the students, give assistance, support learning, provide or take assignments, and provide reinforcement material according to their opted courses. A teacher has a minimum intervention with this process and has the role of a facilitator only. This chapter concludes that the AI-enabled teaching-learning process can't replace the classroom teaching; instead, it is handy. In the future, AI could replace the need of a teacher in class to some extent.

Neo-Symbiosis

2011 ◽  
pp. 2773-2777
Author(s):  
Douglas Griffith ◽  
Frank L. Greitzer
Keyword(s):  
Human Factors ◽  
Human Brain ◽  
Computer Science ◽  
Information Science ◽  
Science And Technology ◽  
Historical Context ◽  
Process Data ◽  
Half Century ◽  
Human Brains ◽  
Computing Machines

The purpose of this article is to re-address the vision of human- computer symbiosis expressed by J. C. R. Licklider nearly a half century ago, when he wrote: “The hope is that in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information- handling machines we know today” (Licklider, 1960). Unfortunately, little progress was made toward this vision over 4 decades following Licklider’s challenge, despite significant advancements in the fields of human factors and computer science. Licklider’s vision was largely forgotten. However, recent advances in information science and technology, psychology, and neuroscience have rekindled the potential of making the Licklider’s vision a reality. This article provides a historical context for and updates the vision, and it argues that such a vision is needed as a unifying framework for advancing IS&T.

Mental illness: The collision of meaning with mechanism

2020 ◽  
pp. 263-289
Author(s):  
Steven E. Hyman ◽  
Doug McConnell
Keyword(s):  
Mental Illness ◽  
Gene Regulation ◽  
Human Brain ◽  
Lived Experience ◽  
Clinical Encounter ◽  
Structure And Function ◽  
Human Beings ◽  
And Function ◽  
Human Brains ◽  
The Brain

‘Mental illness: the collision of meaning with mechanism’ is based on the views of psychiatry that Steven Hyman articulated in his Loebel Lectures—mental illness results from the disordered functioning of the human brain and effective treatment repairs or mitigates those malfunctions. This view is not intended as reductionist as causes of mental illness and contributions to their repair may come from any source that affects the structure and function of the brain. These might include social interactions and other sources of lived experience, ideas (such as those learned in cognitive therapy), gene sequences and gene regulation, metabolic factors, drugs, electrodes, and so on. This, however, is not the whole story for psychiatry on Hyman’s view; interpersonal interactions between clinicians and patients, intuitively understood in such folk psychological terms as selfhood, intention, and agency are also critical for successful practice. As human beings who are suffering, patients seek to make sense of their lives and benefit from the empathy, respect, and a sense of being understood not only as the objects of a clinical encounter, but also as subjects. Hyman’s argument, however, is that the mechanisms by which human brains function and malfunction to produce the symptoms and impairments of mental illness are opaque to introspection and that the mechanistic understandings necessary for diagnosis and treatment are incommensurate with intuitive (folk psychological) human self-understanding. Thus, psychiatry does best when skillful clinicians switch between an objectifying medical and neurobiological stance and the interpersonal stance in which the clinician engages the patients as a subject. Attempts to integrate these incommensurate views of patients and their predicaments have historically produced incoherent explanations of psychopathology and have often led treatment astray. For example, privileging of folk psychological testimony, even when filtered through sophisticated theories has historically led psychiatry into intellectually blind and clinically ineffective cul-de-sacs such as psychoanalysis.

An Educational Method for Theoretical Fields Through Dynamic Visualization

2020 ◽  
pp. 120-148
Author(s):  
Hiroto Namihira
Keyword(s):  
Elementary School ◽  
Human Brain ◽  
Visual Information ◽  
Dynamic Visualization ◽  
Visualization Method ◽  
Educational Method ◽  
Static Information ◽  
Educational Methodology

This chapter proposes a new educational methodology for theoretical contents. It aims to effectively transmit theoretical content meanings. Here, the effects of content visualization enhance the transmission of meaning. By processing visual information, the human brain can immediately understand the mutual relationships between elements in addition to the whole meaning. Comprehension becomes increasingly effective when movement is added to static information. The new educational methodology proposed here is based on such visualization. It is called “The Dynamic Visualization Method.” It is designed so students can visually set allowable conditions before processing them. This selective freedom enables students to extract their hidden leaning interests. Mathematical processes were used to verify the effectiveness of this methodology. A variety of items were thus adopted ranging from the elementary-school to university levels. The contents of those items are visualized in this chapter. The educational effects are then discussed.

Neural substrates of visual percepts, imagery, and hallucinations

2002 ◽  
Vol 25 (2) ◽  
pp. 194-195
Author(s):  
Stephen Grossberg
Keyword(s):  
Visual Attention ◽  
Normal Control ◽  
Mental Imagery ◽  
Visual Information ◽  
Neural Substrates ◽  
Visual Hallucinations ◽  
Neural Models ◽  
Top Down ◽  
Bottom Up

Recent neural models clarify many properties of mental imagery as part of the process whereby bottom-up visual information is influenced by top-down expectations, and how these expectations control visual attention. Volitional signals can transform modulatory top-down signals into supra-threshold imagery. Visual hallucinations can occur when the normal control of these volitional signals is lost.

Modeling the dose distribution in a human brain structure using CT images on the surface of Mars

2020 ◽  
Author(s):  
Salman Khaksarighiri ◽  
Jingnan Guo ◽  
Robert Wimmer-Schweingruber ◽  
Lennart Rostl
Keyword(s):  
Radiation Dose ◽  
Human Brain ◽  
Brain Structure ◽  
Dose Distribution ◽  
Cosmic Radiation ◽  
Cosmic Ray ◽  
Ct Images ◽  
Human Brains ◽  
The Impact ◽  
The Brain

<p>One of the most important steps in the near-future space age will be a manned mission to Mars. Unfortunately, such a mission will cause astronauts to be exposed to unavoidable cosmic radiation in deep space and on the surface of Mars. Thus a better understanding of the radiation environment for a Mars mission and the consequent biological impacts on humans, in particular the human brains, is critical. To investigate the impact of cosmic radiation on human brains and the potential influence on the brain functions, we model and study the cosmic particle-induced radiation dose in a realistic head structure. Specifically speaking, 134 slices of computed tomography (CT) images of an actual human head have been used as a 3D phantom in Geant4 (GEometry ANd Tracking) which is a Monte Carlo tool simulating energetic particles impinging into different parts of the brain and deliver radiation dose therein. As a first step, we compare the influence of different brain structures (e.g., with or without bones, with or without soft tissues) to the resulting dose therein to demonstrate the necessity of using a realistic brain structure for our investigation. Afterwards, we calculate energy-dependent functions of dose distribution for the most important (most abundant and most biologically-relevant) particle types encountered in space and on Mars such as protons, Helium ions and neutrons. These functions are then used to fold with Galactic Cosmic Ray (GCR) spectra on the surface of Mars for obtaining the dose rate distribution at different lobes of the human brain. Different GCR spectra during various solar cycle conditions have also been studied and compared.</p>

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