scholarly journals Computational Models of Face Perception

2017 ◽  
Vol 26 (3) ◽  
pp. 263-269 ◽  
Author(s):  
Aleix M. Martinez
Keyword(s):  
Computer Vision ◽  
Inverse Problem ◽  
Facial Expression ◽  
Computational Modeling ◽  
Face Perception ◽  
Computational Models ◽  
Emotional State ◽  
Current Understanding ◽  
Important Means ◽  
The Brain

Faces are one of the most important means of communication for humans. For example, a short glance at a person’s face provides information about his or her identity and emotional state. What are the computations the brain uses to acquire this information so accurately and seemingly effortlessly? This article summarizes current research on computational modeling, a technique used to answer this question. Specifically, my research tests the hypothesis that this algorithm is tasked with solving the inverse problem of production. For example, to recognize identity, our brain needs to identify shape and shading features that are invariant to facial expression, pose, and illumination. Similarly, to recognize emotion, the brain needs to identify shape and shading features that are invariant to identity, pose, and illumination. If one defines the physics equations that render an image under different identities, expressions, poses, and illuminations, then gaining invariance to these factors can be readily resolved by computing the inverse of this rendering function. I describe our current understanding of the algorithms used by our brains to resolve this inverse problem. I also discuss how these results are driving research in computer vision to design computer systems that are as accurate, robust, and efficient as humans.

Computational Models of Visual Attention

2014 ◽  
pp. 54-76 ◽  
Author(s):  
Rajarshi Pal
Keyword(s):  
Computer Vision ◽  
Visual Attention ◽  
Human Brain ◽  
Computational Modeling ◽  
Computational Models ◽  
Sensory Information ◽  
Processing Capacity ◽  
Human Beings ◽  
Psychological Phenomenon ◽  
External Stimuli

Even the enormous processing capacity of the human brain is not enough to handle all the visual sensory information that falls upon the retina. Still human beings can efficiently respond to the external stimuli. Selective attention plays an important role here. It helps to select only the pertinent portions of the scene being viewed for further processing at the deeper brain. Computational modeling of this neuro-psychological phenomenon has the potential to enrich many computer vision tasks. Enormous amounts of research involving psychovisual experiments and computational models of attention have been and are being carried out all within the past few decades. This article compiles a good volume of these research efforts. It also discusses various aspects related to computational modeling of attention–such as, choice of features, evaluation of these models, and so forth.

Computational Models of Visual Attention

2018 ◽  
pp. 1-26
Author(s):  
Rajarshi Pal
Keyword(s):  
Computer Vision ◽  
Visual Attention ◽  
Human Brain ◽  
Computational Modeling ◽  
Computational Models ◽  
Sensory Information ◽  
Processing Capacity ◽  
Human Beings ◽  
Psychological Phenomenon ◽  
External Stimuli

Even the enormous processing capacity of the human brain is not enough to handle all the visual sensory information that falls upon the retina. Still human beings can efficiently respond to the external stimuli. Selective attention plays an important role here. It helps to select only the pertinent portions of the scene being viewed for further processing at the deeper brain. Computational modeling of this neuro-psychological phenomenon has the potential to enrich many computer vision tasks. Enormous amounts of research involving psychovisual experiments and computational models of attention have been and are being carried out all within the past few decades. This article compiles a good volume of these research efforts. It also discusses various aspects related to computational modeling of attention–such as, choice of features, evaluation of these models, and so forth.

scholarly journals Brain-Inspired Algorithms for Processing of Visual Data

2021 ◽  
pp. 105-115
Author(s):  
Nicola Strisciuglio ◽  
Nicolai Petkov
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Visual Cortex ◽  
Visual System ◽  
Computational Models ◽  
Hierarchical Organization ◽  
Visual Data ◽  
Convolutional Networks ◽  
Improved Stability ◽  
The Brain

AbstractThe study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compose it. These findings inspired researchers in image processing and computer vision to deploy such models to solve problems of visual data processing.In this paper, we review approaches for image processing and computer vision, the design of which is based on neuro-scientific findings about the functions of some neurons in the visual cortex. Furthermore, we analyze the connection between the hierarchical organization of the visual system of the brain and the structure of Convolutional Networks (ConvNets). We pay particular attention to the mechanisms of inhibition of the responses of some neurons, which provide the visual system with improved stability to changing input stimuli, and discuss their implementation in image processing operators and in ConvNets.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

scholarly journals Power Failure of Mitochondria and Oxidative Stress in Neurodegeneration and Its Computational Models

Antioxidants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 229
Author(s):  
JunHyuk Woo ◽  
Hyesun Cho ◽  
YunHee Seol ◽  
Soon Ho Kim ◽  
Chanhyeok Park ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Mitochondrial Dysfunction ◽  
Computational Models ◽  
Neuronal Damage ◽  
Living Organism ◽  
The Body ◽  
Mitochondrial Functions ◽  
A Cell ◽  
The Brain ◽  
And Oxidative Stress

The brain needs more energy than other organs in the body. Mitochondria are the generator of vital power in the living organism. Not only do mitochondria sense signals from the outside of a cell, but they also orchestrate the cascade of subcellular events by supplying adenosine-5′-triphosphate (ATP), the biochemical energy. It is known that impaired mitochondrial function and oxidative stress contribute or lead to neuronal damage and degeneration of the brain. This mini-review focuses on addressing how mitochondrial dysfunction and oxidative stress are associated with the pathogenesis of neurodegenerative disorders including Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, and Parkinson’s disease. In addition, we discuss state-of-the-art computational models of mitochondrial functions in relation to oxidative stress and neurodegeneration. Together, a better understanding of brain disease-specific mitochondrial dysfunction and oxidative stress can pave the way to developing antioxidant therapeutic strategies to ameliorate neuronal activity and prevent neurodegeneration.

scholarly journals Non-Stationary Model of Cerebral Oxygen Transport with Unknown Sources

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 910
Author(s):  
Andrey Kovtanyuk ◽  
Alexander Chebotarev ◽  
Varvara Turova ◽  
Irina Sidorenko ◽  
Renée Lampe
Keyword(s):  
Inverse Problem ◽  
Oxygen Transport ◽  
Model Parameters ◽  
Cerebral Oxygen ◽  
System Of Equations ◽  
Blood Oxygen ◽  
Stationary Model ◽  
Average Oxygen Concentration ◽  
The Right ◽  
The Brain

An inverse problem for a system of equations modeling oxygen transport in the brain is studied. The problem consists of finding the right-hand side of the equation for the blood oxygen transport, which is a linear combination of given functionals describing the average oxygen concentration in the neighborhoods of the ends of arterioles and venules. The overdetermination condition is determined by the values of these functionals evaluated on the solution. The unique solvability of the problem is proven without any smallness assumptions on the model parameters.

scholarly journals Inferring brain-computational mechanisms with models of activity measurements

2016 ◽  
Vol 371 (1705) ◽  
pp. 20160278 ◽  
Author(s):  
Nikolaus Kriegeskorte ◽  
Jörn Diedrichsen
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Summary Statistics ◽  
Activity Data ◽  
Individual Measurement ◽  
Activity Measurements ◽  
The Brain ◽  
The Way

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration

2021 ◽  
Vol 376 (1821) ◽  
pp. 20190765 ◽  
Author(s):  
Giovanni Pezzulo ◽  
Joshua LaPalme ◽  
Fallon Durant ◽  
Michael Levin
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
State Of The Art ◽  
Memory Representation ◽  
Theme Issue ◽  
Evolutionary Innovation ◽  
New Interpretation ◽  
The Brain

Nervous systems’ computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states—in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo . This article is part of the theme issue ‘Basal cognition: multicellularity, neurons and the cognitive lens’.

scholarly journals Psychological home care for type II Schizencephaly: technical and theoretical aspects

2021 ◽  
Vol 70 (4) ◽  
pp. 338-344
Author(s):  
Luísa Pelucio ◽  
Marcia Cristina Nascimento Dourado ◽  
Antonio Egidio Nardi ◽  
Michelle Levitan
Keyword(s):  
Home Care ◽  
Emotional State ◽  
Care Program ◽  
Relaxation Techniques ◽  
Type Ii ◽  
Cognitive Distraction ◽  
Family Orientation ◽  
New Treatment ◽  
Treatment Reality ◽  
The Brain

ABSTRACT Schizencephaly is an extremely rare developmental birth defect or malformation characterized by abnormal clefts in the cerebral hemispheres of the brain, extending from the cortex to the ventricles, which may be unilateral or bilateral. This case report describes the general characteristics of a psychological home care program, reporting the main theoretical and technical elements in a 12-years-old case of type II Schizencephaly. The aims of the psychological home treatment were acceptance of the new treatment reality, a reduction in aggression and anxiety, and psychological support for the patient and family. In the psychological home care, patient’s awareness of illness was developed, along with family orientation, psychoeducation, relaxation techniques, and cognitive distraction. It can be observed that a significant improvement in the affective and emotional state was achieved within the patient’s clinical framework.

scholarly journals Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells

2021 ◽  
Vol 12 ◽  
Author(s):  
Hae Deok Jung ◽  
Yoo Jin Sung ◽  
Hyun Uk Kim
Keyword(s):  
Machine Learning ◽  
Computational Modeling ◽  
Cancer Treatment ◽  
Effective Treatment ◽  
Cancer Cells ◽  
Computational Models ◽  
Omics Data ◽  
Drug Resistant ◽  
Learning Models ◽  
Machine Learning Models

Chemotherapy is a mainstream cancer treatment, but has a constant challenge of drug resistance, which consequently leads to poor prognosis in cancer treatment. For better understanding and effective treatment of drug-resistant cancer cells, omics approaches have been widely conducted in various forms. A notable use of omics data beyond routine data mining is to use them for computational modeling that allows generating useful predictions, such as drug responses and prognostic biomarkers. In particular, an increasing volume of omics data has facilitated the development of machine learning models. In this mini review, we highlight recent studies on the use of multi-omics data for studying drug-resistant cancer cells. We put a particular focus on studies that use computational models to characterize drug-resistant cancer cells, and to predict biomarkers and/or drug responses. Computational models covered in this mini review include network-based models, machine learning models and genome-scale metabolic models. We also provide perspectives on future research opportunities for combating drug-resistant cancer cells.

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