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    Cognitive Neuroscience of Memory Consolidation [[electronic resource] /] : монография / ed.: Axmacher, Nikolai., Rasch, Bjorn. - 1st ed. 2017. - [S. l. : s. n.]. - XV, 417 p. 52 illus., 43 illus. in color. - Б. ц.
    Зміст:
Conceptual Questions of Memory Consolidation --
Memory Consolidation During Off-line Periods and the Role of Sleep --
Mechanisms of Memory Consolidation on a Systems Physiology Level --
Modulation of Memory Consolidation --
Clinical Translation.
Рубрики: Cognitive psychology.
   Neurosciences.
   Biomedical engineering.
   Neurobiology.
   Neural networks (Computer science) .
   Cognitive Psychology.
   Neurosciences.
   Biomedical Engineering and Bioengineering.
   Neurobiology.
   Mathematical Models of Cognitive Processes and Neural Networks.
Анотація: This edited volume provides an overview the state-of-the-art in the field of cognitive neuroscience of memory consolidation. In a number of sections, the editors collect contributions of leading researchers. The topical focus lies on current issues of interest such as memory consolidation including working and long-term memory. In particular, the role of sleep in relation to memory consolidation will be addressed. The target audience primarily comprises research experts in the field of cognitive neuroscience but the book may also be beneficial for graduate students.

Перейти: https://doi.org/10.1007/978-3-319-45066-7

Дод.точки доступу:
Axmacher, Nikolai. \ed.\; Rasch, Bjorn. \ed.\; SpringerLink (Online service)
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    Deep Learning and Convolutional Neural Networks for Medical Image Computing [[electronic resource] :] : precision Medicine, High Performance and Large-Scale Datasets / / ed. Lu, Le. [et al.]. - 1st ed. 2017. - [S. l. : s. n.]. - XIII, 326 p. 117 illus., 100 illus. in color. - Б. ц.
    Зміст:
Part I: Review --
Chapter 1. Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective --
Chapter 2. Review of Deep Learning Methods in Mammography, Cardiovascular and Microscopy Image Analysis --
Part II: Detection and Localization --
Chapter 3. Efficient False-Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation --
Chapter 4. Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning --
Chapter 5. A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set --
Chapter 6. Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers --
Chapter 7. Interstitial Lung Diseases via Deep Convolutional Neural Networks: Segmentation Label Propagation, Unordered Pooling and Cross-Dataset Learning --
Chapter 8. Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging --
Chapter 9. Cell Detection with Deep Learning Accelerated by Sparse Kernel --
Chapter 10. Fully Convolutional Networks in Medical Imaging: Applications to Image Enhancement and Recognition --
Chapter 11. On the Necessity of Fine-Tuned Convolutional Neural Networks for Medical Imaging --
Part III: Segmentation --
Chapter 12. Fully Automated Segmentation Using Distance Regularized Level Set and Deep-Structured Learning and Inference --
Chapter 13. Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms --
Chapter 14. Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local vs. Global Image Context --
Chapter 15. Robust Cell Detection and Segmentation in Histopathological Images using Sparse Reconstruction and Stacked Denoising Autoencoders --
Chapter 16. Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labeling --
Part IV: Big Dataset and Text-Image Deep Mining --
Chapter 17. Interleaved Text/Image Deep Mining on a Large-Scale Radiology Image Database.
Рубрики: Optical data processing.
   Artificial intelligence.
   Neural networks (Computer science) .
   Radiology.
   Image Processing and Computer Vision.
   Artificial Intelligence.
   Mathematical Models of Cognitive Processes and Neural Networks.
   Imaging / Radiology.
Анотація: This timely text/reference presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Topics and features: Highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing Discusses the insightful research experience and views of Dr. Ronald M. Summers in medical imaging-based computer-aided diagnosis and its interaction with deep learning Presents a comprehensive review of the latest research and literature on deep learning for medical image analysis Describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging Examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging Introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database for automated image interpretation This pioneering volume will prove invaluable to researchers and graduate students wishing to employ deep neural network models and representations for medical image analysis and medical imaging applications. Dr. Le Lu is a Staff Scientist in the Radiology and Imaging Sciences Department of the National Institutes of Health Clinical Center, Bethesda, MD, USA. Dr. Yefeng Zheng is a Senior Staff Scientist at Siemens Healthcare Technology Center, Princeton, NJ, USA. Dr. Gustavo Carneiro is an Associate Professor in the School of Computer Science at The University of Adelaide, Australia. Dr. Lin Yang is an Associate Professor in the Department of Biomedical Engineering at the University of Florida, Gainesville, FL, USA.

Перейти: https://doi.org/10.1007/978-3-319-42999-1

Дод.точки доступу:
Lu, Le. \ed.\; Zheng, Yefeng. \ed.\; Carneiro, Gustavo. \ed.\; Yang, Lin. \ed.\; SpringerLink (Online service)
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    Advances in Neural Networks - ISNN 2017 [[electronic resource] :] : 14th International Symposium, ISNN 2017, Sapporo, Hakodate, and Muroran, Hokkaido, Japan, June 21–26, 2017, Proceedings, Part II / / ed.: Cong, Fengyu., Leung, Andrew., Wei, Qinglai. - 1st ed. 2017. - [S. l. : s. n.]. - XXII, 595 p. 251 illus. - Б. ц.
    Зміст:
Clustering, Classification, Modeling, and Forecasting --
Cognition Computation and Neural Networks --
Intelligent Control --
Signal, Image and Video Processing --
Bio-Signal and Medical Image Analysis. .
Рубрики: Pattern recognition.
   Artificial intelligence.
   Computer science—Mathematics.
   Neural networks (Computer science) .
   Algorithms.
   Computer security.
   Pattern Recognition.
   Artificial Intelligence.
   Mathematics of Computing.
   Mathematical Models of Cognitive Processes and Neural Networks.
   Algorithm Analysis and Problem Complexity.
   Systems and Data Security.
Анотація: This book constitutes the refereed proceedings of the 14th International Symposium on Neural Networks, ISNN 2017, held in Sapporo, Hakodate, and Muroran, Hokkaido, Japan, in June 2017. The 135 revised full papers presented in this two-volume set were carefully reviewed and selected from 259 submissions. The papers cover topics like perception, emotion and development, action and motor control, attractor and associative memory, neurodynamics, complex systems, and chaos.

Перейти: https://doi.org/10.1007/978-3-319-59081-3

Дод.точки доступу:
Cong, Fengyu. \ed.\; Leung, Andrew. \ed.\; Wei, Qinglai. \ed.\; SpringerLink (Online service)
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    Advances in Neural Networks - ISNN 2017 [[electronic resource] :] : 14th International Symposium, ISNN 2017, Sapporo, Hakodate, and Muroran, Hokkaido, Japan, June 21–26, 2017, Proceedings, Part I / / ed.: Cong, Fengyu., Leung, Andrew., Wei, Qinglai. - 1st ed. 2017. - [S. l. : s. n.]. - XXII, 583 p. 238 illus. - Б. ц.
    Зміст:
Clustering, Classification, Modeling, and Forecasting --
Cognition Computation and Neural Networks --
Intelligent Control --
Signal, Image and Video Processing --
Bio-Signal and Medical Image Analysis.
Рубрики: Pattern recognition.
   Artificial intelligence.
   Computer science—Mathematics.
   Neural networks (Computer science) .
   Algorithms.
   Computer security.
   Pattern Recognition.
   Artificial Intelligence.
   Mathematics of Computing.
   Mathematical Models of Cognitive Processes and Neural Networks.
   Algorithm Analysis and Problem Complexity.
   Systems and Data Security.
Анотація: This book constitutes the refereed proceedings of the 14th International Symposium on Neural Networks, ISNN 2017, held in Sapporo, Hakodate, and Muroran, Hokkaido, Japan, in June 2017. The 135 revised full papers presented in this two-volume set were carefully reviewed and selected from 259 submissions. The papers cover topics like perception, emotion and development, action and motor control, attractor and associative memory, neurodynamics, complex systems, and chaos.

Перейти: https://doi.org/10.1007/978-3-319-59072-1

Дод.точки доступу:
Cong, Fengyu. \ed.\; Leung, Andrew. \ed.\; Wei, Qinglai. \ed.\; SpringerLink (Online service)
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    Mendel, Jerry M.
    Uncertain Rule-Based Fuzzy Systems [[electronic resource] :] : introduction and New Directions, 2nd Edition / / Jerry M. Mendel ; . - 2nd ed. 2017. - [S. l. : s. n.]. - XXII, 684 p. 215 illus., 192 illus. in color. - Б. ц.
    Зміст:
Introduction --
Part 1: Type-1 Fuzzy Sets and Systems --
Short Primers on Type-1 Fuzzy Sets and Fuzzy Logic --
Type-1 Fuzzy Logic Systems --
Part 2: Type-2 Fuzzy Sets --
Sources of Uncertainty --
Type-2 Fuzzy Sets --
Operations on and Properties OF Type-2 Fuzzy Sets --
Type-2 Relations and Compositions --
Centroid of a Type-2 Fuzzy Set: Type-Reduction --
Part 3: Type-2 Fuzzy Logic Systems --
Mamdani Interval Type-2 Fuzzy Logic Systems (IT2 FLSS) --
TSK Interval Type-2 Fuzzy Logic Systems --
General Type-2 Fuzzy Logic Systems (GT2 FLSS) --
Conclusion.
Рубрики: Electrical engineering.
   Computational intelligence.
   Artificial intelligence.
   Neural networks (Computer science) .
   Communications Engineering, Networks.
   Computational Intelligence.
   Artificial Intelligence.
   Mathematical Models of Cognitive Processes and Neural Networks.
Анотація: The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that can model uncertainty — i.e., “type-2” fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications from time-series forecasting to knowledge mining to control. In this new edition, a bottom-up approach is presented that begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems – from type-1 to interval type-2 to general type-2 – in one volume. For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material. Presents fully updated material on new breakthroughs in human-inspired rule-based techniques for handling real-world uncertainties; Allows those already familiar with type-1 fuzzy sets and systems to rapidly come up to speed to type-2 fuzzy sets and systems; Features complete classroom material including end-of-- Chapter exercises, a solutions manual, and three case studies -- forecasting of time series to knowledge mining from surveys and PID control.

Перейти: https://doi.org/10.1007/978-3-319-51370-6

Дод.точки доступу:
Mendel, Jerry M. \.\; SpringerLink (Online service)
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    Florescu, Dorian.
    Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits [[electronic resource] /] : монография / Dorian. Florescu ; . - 1st ed. 2017. - [S. l. : s. n.]. - XIV, 139 p. 42 illus., 27 illus. in color. - Б. ц.
    Зміст:
Nomenclature --
Acronyms --
1 Introduction --
2 Time Encoding and Decoding in Bandlimited and Shift-Invariant Spaces --
3 A Novel Framework for Reconstructing Bandlimited Signals Encoded by Integrate and-Fire Neurons --
4 A Novel Reconstruction Framework in Shift-Invariant Spaces for Signals Encoded with Integrate-and-Fire Neurons --
5 A New Approach to the Identification of Sensory Processing Circuits Based on Spiking Neuron Data --
6 A New Method for Implementing Linear Filters in the Spike Domain --
7 Conclusions and Future Work --
Bibliography.
Рубрики: Signal processing.
   Image processing.
   Speech processing systems.
   Neural networks (Computer science) .
   Neurosciences.
   System theory.
   Electronic circuits.
   Signal, Image and Speech Processing.
   Mathematical Models of Cognitive Processes and Neural Networks.
   Neurosciences.
   Systems Theory, Control.
   Circuits and Systems.
Анотація: This work is motivated by the ongoing open question of how information in the outside world is represented and processed by the brain. Consequently, several novel methods are developed. A new mathematical formulation is proposed for the encoding and decoding of analog signals using integrate-and-fire neuron models. Based on this formulation, a novel algorithm, significantly faster than the state-of-the-art method, is proposed for reconstructing the input of the neuron. Two new identification methods are proposed for neural circuits comprising a filter in series with a spiking neuron model. These methods reduce the number of assumptions made by the state-of-the-art identification framework, allowing for a wider range of models of sensory processing circuits to be inferred directly from input-output observations. A third contribution is an algorithm that computes the spike time sequence generated by an integrate-and-fire neuron model in response to the output of a linear filter, given the input of the filter encoded with the same neuron model.

Перейти: https://doi.org/10.1007/978-3-319-57081-5

Дод.точки доступу:
Florescu, Dorian. \.\; SpringerLink (Online service)
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    da Silva, Silva, Ivan Nunes.
    Artificial Neural Networks [[electronic resource] :] : a Practical Course / / Silva, Ivan Nunes. da Silva, Hernane Spatti, Danilo. [et al.] ; . - 1st ed. 2017. - [S. l. : s. n.]. - XX, 307 p. 203 illus., 13 illus. in color. - Б. ц.
    Зміст:
Introduction --
PART I – Neural Networks Architectures and Their Theoretical Aspects --
Architectures of Artificial Neural Networks and Training Processes --
Perceptron Network and Learning Rule --
Adaline Network and Delta Rule --
Multilayer Perceptron (MLP) --
Radial Basis Function (RBF) --
Recurrent Neural Topologies and Hopfield Network --
Self-Organizing Maps and Kohonen Network --
Learning Vector Quantization (LVQ) and Counter-Propagation Network --
Adaptive Resonance Theory (ART) --
Part II – Artificial Neural Networks Applications in Problems of Engineering and Applied Sciences --
Coffee Global Quality Estimation Using Multilayer Perceptron --
Computer Network Traffic Analysis Using SNMP Protocol and LVQ Network --
Forecasting Stock Market Trends Using Recurrent Network --
System for Disease Diagnosis Using ART Network --
Adulterants Patterns Identification in Coffee Powder Using Self-Organizing Maps --
Disturbances Recognition Related to Electrical Power Quality Using PMC Network --
Mobile Robot Trajectory Control Using Fuzzy System and MLP Network --
Method to Tomatoes Classification Using Computer Vision and MLP Network --
Analysis of RBF and MLP Network Performance in Pattern Classification Problems --
Solving Constrained Optimization Problems Using Hopfield Network --
Conclusion.
Рубрики: Electrical engineering.
   Computational intelligence.
   Neural networks (Computer science) .
   Data mining.
   Pattern recognition.
   Communications Engineering, Networks.
   Computational Intelligence.
   Mathematical Models of Cognitive Processes and Neural Networks.
   Data Mining and Knowledge Discovery.
   Pattern Recognition.
Анотація: This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

Перейти: https://doi.org/10.1007/978-3-319-43162-8

Дод.точки доступу:
Hernane Spatti, Danilo.; Andrade Flauzino, Rogerio.; Liboni, Luisa Helena Bartocci.; dos Reis Alves, Silas Franco.; da Silva, Ivan Nunes. \.\; SpringerLink (Online service)
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    Modeling Cellular Systems [[electronic resource] /] : монография / ed.: Graw, Frederik., Matthaus, Franziska., Pahle, Jurgen. - 1st ed. 2017. - [S. l. : s. n.]. - XI, 161 p. 35 illus., 29 illus. in color. - Б. ц.
    Зміст:
Numerical treatment of the Filament Based Lamellipodium Model (FBLM) --
Understanding the role of mitochondria distribution in calcium dynamics and secretion in bovine chromaffin cells --
IL-2 stimulation of regulatory T cells: a stochastic and algorithmic approach --
Spatio-Temporal Modeling of Membrane Receptors --
Distribution approximations for the chemical master equation: comparison of the method of moments and the system size expansion --
Dynamical features of the MAP kinase cascade --
Sampling from T cell receptor repertoires.
Рубрики: Biomedical engineering.
   Biomathematics.
   Systems biology.
   Biological systems.
   Neural networks (Computer science) .
   Biomedical Engineering and Bioengineering.
   Physiological, Cellular and Medical Topics.
   Systems Biology.
   Systems Biology.
   Mathematical Models of Cognitive Processes and Neural Networks.
Анотація: This contributed volume comprises research articles and reviews on topics connected to the mathematical modeling of cellular systems. These contributions cover signaling pathways, stochastic effects, cell motility and mechanics, pattern formation processes, as well as multi-scale approaches. All authors attended the workshop on "Modeling Cellular Systems" which took place in Heidelberg in October 2014. The target audience primarily comprises researchers and experts in the field, but the book may also be beneficial for graduate students.

Перейти: https://doi.org/10.1007/978-3-319-45833-5

Дод.точки доступу:
Graw, Frederik. \ed.\; Matthaus, Franziska. \ed.\; Pahle, Jurgen. \ed.\; SpringerLink (Online service)
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    Computational Neurology and Psychiatry [[electronic resource] /] : монография / ed.: Erdi, Peter., Sen Bhattacharya, Basabdatta., Cochran, Amy L. - 1st ed. 2017. - [S. l. : s. n.]. - VI, 448 p. 157 illus., 119 illus. in color. - Б. ц.
    Зміст:
From the Content --
Introduction --
Outgrowing Neurological Diseases: Microcircuits, Conduction Delay and Dynamic Diseases --
Extracellular Potassium and Focal Seizures – Insight From in Silico Study --
Time Series and Interactions: Data Processing in Epilepsy Research.
Рубрики: Computational intelligence.
   Computer simulation.
   Neurosciences.
   Neural networks (Computer science) .
   Computational Intelligence.
   Simulation and Modeling.
   Neurosciences.
   Mathematical Models of Cognitive Processes and Neural Networks.
Анотація: This book presents the latest research in computational methods for modeling and simulating brain disorders. In particular, it shows how mathematical models can be used to study the relationship between a given disorder and the specific brain structure associated with that disorder. It also describes the emerging field of computational psychiatry, including the study of pathological behavior due to impaired functional connectivity, pathophysiological activity, and/or aberrant decision-making. Further, it discusses the data analysis techniques that will be required to analyze the increasing amount of data being generated about the brain. Lastly, the book offers some tips on the application of computational models in the field of quantitative systems pharmacology. Mainly written for computational scientists eager to discover new application fields for their model, this book also benefits neurologists and psychiatrists wanting to learn about new methods.

Перейти: https://doi.org/10.1007/978-3-319-49959-8

Дод.точки доступу:
Erdi, Peter. \ed.\; Sen Bhattacharya, Basabdatta. \ed.\; Cochran, Amy L. \ed.\; SpringerLink (Online service)
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10.

    Iatan, Iuliana F.
    Issues in the Use of Neural Networks in Information Retrieval [[electronic resource] /] : монография / Iuliana F. Iatan ; . - 1st ed. 2017. - [S. l. : s. n.]. - XIX, 199 p. 88 illus., 44 illus. in color. - Б. ц.
    Зміст:
Mathematical Aspects of Using Neural Approaches for Information Retrieval --
A Fuzzy Kwan- Cai Neural Network for Determining Image Similarity and for the Face Recognition --
Predicting Human Personality from Social Media using a Fuzzy Neural Network --
Modern Neural Methods for Function Approximation --
A Fuzzy Gaussian Clifford Neural Network --
Concurrent Fuzzy Neural Networks --
A New Interval Arithmetic Based Neural Network --
A Recurrent Neural Fuzzy Network.
Рубрики: Computational intelligence.
   Artificial intelligence.
   Neural networks (Computer science) .
   Pattern recognition.
   Computational Intelligence.
   Artificial Intelligence.
   Mathematical Models of Cognitive Processes and Neural Networks.
   Pattern Recognition.
Анотація: This book highlights the ability of neural networks (NNs) to be excellent pattern matchers and their importance in information retrieval (IR), which is based on index term matching. The book defines a new NN-based method for learning image similarity and describes how to use fuzzy Gaussian neural networks to predict personality. It introduces the fuzzy Clifford Gaussian network, and two concurrent neural models: (1) concurrent fuzzy nonlinear perceptron modules, and (2) concurrent fuzzy Gaussian neural network modules. Furthermore, it explains the design of a new model of fuzzy nonlinear perceptron based on alpha level sets and describes a recurrent fuzzy neural network model with a learning algorithm based on the improved particle swarm optimization method.

Перейти: https://doi.org/10.1007/978-3-319-43871-9

Дод.точки доступу:
Iatan, Iuliana F. \.\; SpringerLink (Online service)
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11.

    Advances in Memristors, Memristive Devices and Systems [[electronic resource] /] : монография / ed.: Vaidyanathan, Sundarapandian., Volos, Christos. - 1st ed. 2017. - [S. l. : s. n.]. - XII, 511 p. 294 illus., 229 illus. in color. - Б. ц.
    Зміст:
Chapter 1 Memristor Emulators A Note on Modeling --
Chapter 2 A Simple Oscillator using Memristor --
Chapter 3 A Hyperjerk Memristive System with Hidden Attractors --
Chapter 4 A Memristive System with Hidden Attractors and its Engineering Application --
Chapter 5 Adaptive Control, Synchronization and Circuit Simulation of a Memristor-Based --
Chapter 6 Modern System Design using Memristors --
Chapter 7 RF/Microwave Applications of Memristors --
Chapter 8 Theory, Modeling and Design of Memristor-Based Min-Max Circuits --
Chapter 9 Analysis of a 4-D Hyperchaotic Fractional-Order Memristive System with Hidden Attractors --
Chapter 10 Adaptive Control and Synchronization of a Memristor-Based Shinriki’s System.
Рубрики: Computational intelligence.
   Electronic circuits.
   Electronics.
   Microelectronics.
   Neural networks (Computer science) .
   Computational Intelligence.
   Circuits and Systems.
   Electronics and Microelectronics, Instrumentation.
   Mathematical Models of Cognitive Processes and Neural Networks.
Анотація: This book reports on the latest advances in and applications of memristors, memristive devices and systems. It gathers 20 contributed chapters by subject experts, including pioneers in the field such as Leon Chua (UC Berkeley, USA) and R.S. Williams (HP Labs, USA), who are specialized in the various topics addressed in this book, and covers broad areas of memristors and memristive devices such as: memristor emulators, oscillators, chaotic and hyperchaotic memristive systems, control of memristive systems, memristor-based min-max circuits, canonic memristors, memristive-based neuromorphic applications, implementation of memristor-based chaotic oscillators, inverse memristors, linear memristor devices, delayed memristive systems, flux-controlled memristive emulators, etc. Throughout the book, special emphasis is given to papers offering practical solutions and design, modeling, and implementation insights to address current research problems in memristors, memristive devices and systems. As such, it offers a valuable reference book on memristors and memristive devices for graduate students and researchers with a basic knowledge of electrical and control systems engineering.

Перейти: https://doi.org/10.1007/978-3-319-51724-7

Дод.точки доступу:
Vaidyanathan, Sundarapandian. \ed.\; Volos, Christos. \ed.\; SpringerLink (Online service)
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12.

    Anticipation and Medicine [[electronic resource] /] : монография / ed. Nadin, Mihai. - 1st ed. 2017. - [S. l. : s. n.]. - IX, 363 p. 49 illus., 31 illus. in color. - Б. ц.
    Зміст:
Part I Anticipation and Medical Care --
Part II Evaluating the Risk Factors and Opportunities of New Medical Procedures --
Part III Examining the Brain --
Part IV Anticipation and Medical Data Processing --
Part V Anticipation and Psychological Aspects of Patient Treatments --
Part VI Anticipation and Ubiquitous Computing --
Part VII Anticipation and Alternative Medicine.
Рубрики: Computational intelligence.
   Neural networks (Computer science) .
   Artificial intelligence.
   Computational Intelligence.
   Mathematical Models of Cognitive Processes and Neural Networks.
   Artificial Intelligence.
Анотація: In this book, practicing physicians and experts in anticipation present arguments for a new understanding of medicine. Their contributions make it clear that medicine is the decisive test for anticipation. The reader is presented with a provocative hypothesis: If medicine will align itself with the anticipatory condition of life, it can prompt the most important revolution in our time. To this end, all stakeholders—medical practitioners, patients, scientists, and technology developers—will have to engage in the conversation. The book makes the case for the transition from expensive, and only marginally effective, reactive treatment through “spare parts” (joint replacements, organ transplants) and reliance on pharmaceuticals (antibiotics, opiates) to anticipation-informed healthcare. Readers will understand why the current premise of treating various behavioral conditions (attention deficit disorder, hyperactivity, schizophrenia) through drugs has to be re-evaluated from the perspective of anticipation. In the manner practiced today, medicine generates dependence and long-lasting damage to those it is paid to help. As we better understand the nature of the living, the proactive view of healthcare, within which the science and art of healing fuse, becomes a social and political mandate.

Перейти: https://doi.org/10.1007/978-3-319-45142-8

Дод.точки доступу:
Nadin, Mihai. \ed.\; SpringerLink (Online service)
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13.

    Language in Complexity [[electronic resource] :] : the Emerging Meaning / / ed.: La Mantia, Francesco., Licata, Ignazio., Perconti, Pietro. - 1st ed. 2017. - [S. l. : s. n.]. - XXVI, 199 p. 18 illus., 12 illus. in color. - Б. ц.
    Зміст:
Introduction.-The Game of Complexity and Linguistic Theorization --
Continuity in the Interactions between Linguistic Units --
Modeling Language Change --
The Case for Cognitive Plausibility --
System and Structure --
Hjelmslev and the Stratification of Signs and Language --
From Topology to Quasi-Topology --
Fiat Lux versus Fiat Lumen --
Two Ways into Complexity --
Language and Brain Complexity.
Рубрики: Computational complexity.
   Computational linguistics.
   Natural language processing (Computer science).
   Neural networks (Computer science) .
   Neurosciences.
   Complexity.
   Computational Linguistics.
   Natural Language Processing (NLP).
   Mathematical Models of Cognitive Processes and Neural Networks.
   Neurosciences.
Анотація: This contributed volume explores the achievements gained and the remaining puzzling questions by applying dynamical systems theory to the linguistic inquiry. In particular, the book is divided into three parts, each one addressing one of the following topics: 1) Facing complexity in the right way: mathematics and complexity 2) Complexity and theory of language 3) From empirical observation to formal models: investigation of specific linguistic phenomena, like enunciation, deixis, or the meaning of the metaphorical phrases The application of complexity theory to describe cognitive phenomena is a recent and very promising trend in cognitive science. At the time when dynamical approaches triggered a paradigm shift in cognitive science some decade ago, the major topic of research were the challenges imposed by classical computational approaches dealing with the explanation of cognitive phenomena like consciousness, decision making and language. The target audience primarily comprises researchers and experts in the field but the book may also be beneficial for graduate and post-graduate students who want to enter the field.

Перейти: https://doi.org/10.1007/978-3-319-29483-4

Дод.точки доступу:
La Mantia, Francesco. \ed.\; Licata, Ignazio. \ed.\; Perconti, Pietro. \ed.\; SpringerLink (Online service)
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14.

    Kong, Xiangyu.
    Principal Component Analysis Networks and Algorithms [[electronic resource] /] : монография / Xiangyu. Kong, Hu, Changhua., Duan, Zhansheng. ; . - 1st ed. 2017. - [S. l. : s. n.]. - XXII, 323 p. 86 illus., 41 illus. in color. - Б. ц.
    Зміст:
Introduction --
Eigenvalue and singular value decomposition --
Principal component analysis neural networks --
Minor component analysis neural networks --
Dual purpose methods for principal and minor component analysis --
Deterministic discrete time system for PCA or MCA methods --
Generalized feature extraction method --
Coupled principal component analysis --
Singular feature extraction neural networks.
Рубрики: Computational intelligence.
   Pattern recognition.
   Neural networks (Computer science) .
   Statistics .
   Algorithms.
   Signal processing.
   Image processing.
   Speech processing systems.
   Computational Intelligence.
   Pattern Recognition.
   Mathematical Models of Cognitive Processes and Neural Networks.
   Statistical Theory and Methods.
   Algorithm Analysis and Problem Complexity.
   Signal, Image and Speech Processing.
Анотація: This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.

Перейти: https://doi.org/10.1007/978-981-10-2915-8

Дод.точки доступу:
Hu, Changhua.; Duan, Zhansheng.; Kong, Xiangyu. \.\; SpringerLink (Online service)
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15.

    Borgers, Christoph.
    An Introduction to Modeling Neuronal Dynamics [[electronic resource] /] : монография / Christoph. Borgers ; . - 1st ed. 2017. - [S. l. : s. n.]. - XIII, 457 p. 356 illus., 186 illus. in color. - Б. ц.
    Зміст:
Vocabulary and Notation --
Modeling a Single Neuron --
The Nernst Equilibrium --
The Classical Hodgkin-Huxley ODEs --
Numerical Solution of the Hodgkin-Huxley ODEs --
Three Simple Models of Neurons in Rodent Brains --
The Classical Hodgkin-Huxley PDEs --
Linear Integrate-and-fire (LIF) Neurons --
Quadratic Integrate-and-fire (QIF) and Theta Neurons --
Spike Frequency Adaptation --
Dynamics of Single Neuron Models --
The Slow-fast Phase Plane --
Saddle-node Collisions --
Model Neurons of Bifurcation Type 1 --
Hopf Bifurcations --
Model Neurons of Bifurcation Type 2 --
Canard Explosions --
Model Neurons of Bifurcation Type 3 --
Frequency-current Curves --
Bistability Resulting from Rebound Firing --
Bursting --
Modeling Nuronal Communication --
Chemical Synapses --
Gap Junctions --
A Wilson-Cowan Model of an Oscillatory E-I Network --
Entertainment, Synchronization, and Oscillations --
Entertainment by Excitatory Input Pulses --
Synchronization by Fast Recurrent Excitation --
Phase Response Curves (PRCs) --
Synchronization of Two Pulse-coupled Oscillators --
Oscillators Coupled by Delayed Pulses --
Weakly Coupled Oscillators --
Approximate Synchronization by a Single Inhibitory Pulse --
The PING Model of Gamma Rhythms --
ING Rhythms --
Weak PING Rhythms --
Beta Rhythms --
Nested Gamma-theta Rhythms --
Functional Significance of Synchrony and Oscillations --
Rhythmic vs. Tonic Inhibition --
Rhythmic vs. Tonic Excitation --
Gamma Rhythms and Cell Assemblies --
Gamma Rhythms and Communication --
Synaptic Plasticity --
Short-term Depression and Facilitation --
Spike Timing-dependent Plasticity (STDP) --
Appendices --
A. The Bisection Method --
Fixed Point Iteration --
Elementary Probability Theory --
Smooth Approximations of Non-smooth Functions --
Solutions to Selected Homework Problems.
Рубрики: Neural networks (Computer science) .
   Biomathematics.
   Neurosciences.
   Statistical physics.
   Vibration.
   Dynamical systems.
   Dynamics.
   Mathematical Models of Cognitive Processes and Neural Networks.
   Mathematical and Computational Biology.
   Neurosciences.
   Statistical Physics and Dynamical Systems.
   Vibration, Dynamical Systems, Control.
Анотація: This book is intended as a text for a one-semester course on Mathematical and Computational Neuroscience for upper-level undergraduate and beginning graduate students of mathematics, the natural sciences, engineering, or computer science. An undergraduate introduction to differential equations is more than enough mathematical background. Only a slim, high school-level background in physics is assumed, and none in biology. Topics include models of individual nerve cells and their dynamics, models of networks of neurons coupled by synapses and gap junctions, origins and functions of population rhythms in neuronal networks, and models of synaptic plasticity. An extensive online collection of Matlab programs generating the figures accompanies the book. .

Перейти: https://doi.org/10.1007/978-3-319-51171-9

Дод.точки доступу:
Borgers, Christoph. \.\; SpringerLink (Online service)
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16.

    Neuman, Yair.
    Mathematical Structures of Natural Intelligence [[electronic resource] /] : монография / Yair. Neuman ; . - 1st ed. 2017. - [S. l. : s. n.]. - XVII, 173 p. 39 illus., 6 illus. in color. - Б. ц.
    Зміст:
Part I. 1. Introduction --
2. What is Structure? --
3. Category Theory --
4. How to Trick the Demon of Entropy --
5. Neural Networks and Groupoids --
Part II. 6. Natural Intelligence in the Wild --
7. Natural Intelligence is about meaning --
8. From Identity to Equivalence --
9. On Negation --
10. Modeling --
11. On Structures and Wholes --
Part III. 12. Let's Talk About Nothing --
13. King Richard is a Lion --
14. The Madman and the Dentist --
15. Discussion --
References --
Author index --
Subject index.
Рубрики: Category theory (Mathematics).
   Homological algebra.
   Neural networks (Computer science) .
   Algebraic topology.
   Category Theory, Homological Algebra.
   Mathematical Models of Cognitive Processes and Neural Networks.
   Algebraic Topology.
Анотація: This book uncovers mathematical structures underlying natural intelligence and applies category theory as a modeling language for understanding human cognition, giving readers new insights into the nature of human thought. In this context, the book explores various topics and questions, such as the human representation of the number system, why our counting ability is different from that which is evident among non-human organisms, and why the idea of zero is so difficult to grasp. The book is organized into three parts: the first introduces the general reason for studying general structures underlying the human mind; the second part introduces category theory as a modeling language and use it for exposing the deep and fascinating structures underlying human cognition; and the third applies the general principles and ideas of the first two parts to reaching a better understanding of challenging aspects of the human mind such as our understanding of the number system, the metaphorical nature of our thinking and the logic of our unconscious dynamics. About the Author: Yair Neuman is a Full Professor at Ben-Gurion University. He holds a BA in Psychology (Major) and Philosophy (Minor) and a PhD in Cognition (Hebrew University, 1999), and his expertise is in studying complex cognitive, social, and symbolic systems from a unique interdisciplinary approach. Professor Neuman has published numerous papers and five academic books and has been a visiting scholar or professor at MIT, the University of Toronto, the University of Oxford, and the Weizmann Institute of Science. Beyond his purely academic work, he has developed state-of-the-art algorithms for social and cognitive computing, such as those he developed for the IARPA metaphor project (ADAMA group).

Перейти: https://doi.org/10.1007/978-3-319-68246-4

Дод.точки доступу:
Neuman, Yair. \.\; SpringerLink (Online service)
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17.

    Petitot, Jean.
    Elements of Neurogeometry [[electronic resource] :] : functional Architectures of Vision / / Jean. Petitot ; . - 1st ed. 2017. - [S. l. : s. n.]. - XV, 379 p. 257 illus., 186 illus. in color. - Б. ц.
    Зміст:
Preface --
Introdcution --
Receptive Fields and Profiles, and Wavelet Analysis --
Pinwheels of V1 Horizontal Connections and Contact Structure --
Transition to Volume II --
References.
Рубрики: Biomathematics.
   Neural networks (Computer science) .
   Geometry.
   Mathematical and Computational Biology.
   Mathematical Models of Cognitive Processes and Neural Networks.
   Geometry.
Анотація: This book describes several mathematical models of the primary visual cortex, referring them to a vast ensemble of experimental data and putting forward an original geometrical model for its functional architecture, that is, the highly specific organization of its neural connections. The book spells out the geometrical algorithms implemented by this functional architecture, or put another way, the “neurogeometry” immanent in visual perception. Focusing on the neural origins of our spatial representations, it demonstrates three things: firstly, the way the visual neurons filter the optical signal is closely related to a wavelet analysis; secondly, the contact structure of the 1-jets of the curves in the plane (the retinal plane here) is implemented by the cortical functional architecture; and lastly, the visual algorithms for integrating contours from what may be rather incomplete sensory data can be modelled by the sub-Riemannian geometry associated with this contact structure. As such, it provides readers with the first systematic interpretation of a number of important neurophysiological observations in a well-defined mathematical framework. The book’s neuromathematical exploration appeals to graduate students and researchers in integrative-functional-cognitive neuroscience with a good mathematical background, as well as those in applied mathematics with an interest in neurophysiology.

Перейти: https://doi.org/10.1007/978-3-319-65591-8

Дод.точки доступу:
Petitot, Jean. \.\; SpringerLink (Online service)
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18.

    Lubashevsky, Ihor.
    Physics of the Human Mind [[electronic resource] /] : монография / Ihor. Lubashevsky ; . - 1st ed. 2017. - [S. l. : s. n.]. - XIV, 380 p. 83 illus., 41 illus. in color. - Б. ц.
    Зміст:
Modeling of Human Behavior as Individual Branch of Physics and Mathematics --
Why Laws of Classical Physics Have Their Form --
Fodor-Kim Dilemma --
Strong Emergence via Constitutive Fields --
Non-Cartesian Dualism and Meso-Relational Media --
Modeling of Human Behavior Within the Paradigm of Modern Physics --
Emergent Phenomena Caused by Bounded Capacity of Human Cognition --
Epilog: Physics and Human Mind --
References --
Index.
Рубрики: Sociophysics.
   Econophysics.
   Neural networks (Computer science) .
   Cognitive psychology.
   Philosophy of mind.
   Physics.
   Data-driven Science, Modeling and Theory Building.
   Mathematical Models of Cognitive Processes and Neural Networks.
   Cognitive Psychology.
   Philosophy of Mind.
   Mathematical Methods in Physics.
Анотація: This book tackles the challenging question which mathematical formalisms and possibly new physical notions should be developed for quantitatively describing human cognition and behavior, in addition to the ones already developed in the physical and cognitive sciences. Indeed, physics is widely used in modeling social systems, where, in particular, new branches of science such as sociophysics and econophysics have arisen. However, many if not most characteristic features of humans like willingness, emotions, memory, future prediction, and moral norms, to name but a few, are not yet properly reflected in the paradigms of physical thought and theory. The choice of a relevant formalism for modeling mental phenomena requires the comprehension of the general philosophical questions related to the mind-body problem. Plausible answers to these questions are investigated and reviewed, notions and concepts to be used or to be taken into account are developed and some challenging questions are posed as open problems. This text addresses theoretical physicists and neuroscientists modeling any systems and processes where human factors play a crucial role, philosophers interested in applying philosophical concepts to the construction of mathematical models, and the mathematically oriented psychologists and sociologists, whose research is fundamentally related to modeling mental processes.

Перейти: https://doi.org/10.1007/978-3-319-51706-3

Дод.точки доступу:
Lubashevsky, Ihor. \.\; SpringerLink (Online service)
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19.

    Emergent Complexity from Nonlinearity, in Physics, Engineering and the Life Sciences [[electronic resource] :] : proceedings of the XXIII International Conference on Nonlinear Dynamics of Electronic Systems, Como, Italy, 7-11 September 2015 / / ed.: Mantica, Giorgio., Stoop, Ruedi., Stramaglia, Sebastiano. - 1st ed. 2017. - [S. l. : s. n.]. - XXV, 222 p. 113 illus., 86 illus. in color. - Б. ц.
Рубрики: Statistical physics.
   Dynamical systems.
   Electronics.
   Microelectronics.
   Systems biology.
   Neural networks (Computer science) .
   Biochemistry.
   Complex Systems.
   Electronics and Microelectronics, Instrumentation.
   Systems Biology.
   Mathematical Models of Cognitive Processes and Neural Networks.
   Biochemistry, general.
   Statistical Physics and Dynamical Systems.
Анотація: This book collects contributions to the XXIII international conference “Nonlinear dynamics of electronic systems”. Topics range from non-linearity in electronic circuits to synchronisation effects in complex networks to biological systems, neural dynamics and the complex organisation of the brain. Resting on a solid mathematical basis, these investigations address highly interdisciplinary problems in physics, engineering, biology and biochemistry.

Перейти: https://doi.org/10.1007/978-3-319-47810-4

Дод.точки доступу:
Mantica, Giorgio. \ed.\; Stoop, Ruedi. \ed.\; Stramaglia, Sebastiano. \ed.\; SpringerLink (Online service)
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20.

    Pinna, Simone.
    Extended Cognition and the Dynamics of Algorithmic Skills [[electronic resource] /] : монография / Simone. Pinna ; . - 1st ed. 2017. - [S. l. : s. n.]. - XXVII, 122 p. 5 illus. - Б. ц.
    Зміст:
Turing’s Theory of Computation --
Cognition as Organism-environment Interaction --
Ecological Approach and Dynamical Approach --
Modeling Algorithmic Skills: the Bidimensional Turing Machine --
BTM Models of Algorithmic Skills.
Рубрики: Philosophy of mind.
   Computers.
   Neural networks (Computer science) .
   Cognitive psychology.
   Philosophy of Mind.
   Computation by Abstract Devices.
   Mathematical Models of Cognitive Processes and Neural Networks.
   Cognitive Psychology.
Анотація: This book describes a novel methodology for studying algorithmic skills, intended as cognitive activities related to rule-based symbolic transformation, and argues that some human computational abilities may be interpreted and analyzed as genuine examples of extended cognition. It shows that the performance of these abilities relies not only on innate neurocognitive systems or language-related skills, but also on external tools and general agent–environment interactions. Further, it asserts that a low-level analysis, based on a set of core neurocognitive systems linking numbers and language, is not sufficient to explain some specific forms of high-level numerical skills, like those involved in algorithm execution. To this end, it reports on the design of a cognitive architecture for modeling all the relevant features involved in the execution of algorithmic strategies, including external tools, such as paper and pencils. The first part of the book discusses the philosophical premises for endorsing and justifying a position in philosophy of mind that links a modified form of computationalism with some recent theoretical and scientific developments, like those introduced by the so-called dynamical approach to cognition. The second part is dedicated to the description of a Turing-machine-inspired cognitive architecture, expressly designed to formalize all kinds of algorithmic strategies.

Перейти: https://doi.org/10.1007/978-3-319-51841-1

Дод.точки доступу:
Pinna, Simone. \.\; SpringerLink (Online service)
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(Асоціація ЕБНІТ)