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Natural and Artificial Computation for Biomedicine and Neuroscience

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Cover of 'Natural and Artificial Computation for Biomedicine and Neuroscience'

Table of Contents

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    Book Overview
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    Chapter 1 Robot’s and Human’s Self: A Computational Perspective
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    Chapter 2 A Neurologically Inspired Network Model for Graziano’s Attention Schema Theory for Consciousness
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    Chapter 3 Multilevel Darwinist Brain: Context Nodes in a Network Memory Inspired Long Term Memory
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    Chapter 4 Motivational Engine for Cognitive Robotics in Non-static Tasks
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    Chapter 5 Execution of Written Tasks by a Biologically-Inspired Artificial Brain
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    Chapter 6 A Cognitive Architecture Framework for Critical Situation Awareness Systems
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    Chapter 7 A Conductance-Based Neuronal Network Model for Color Coding in the Primate Foveal Retina
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    Chapter 8 Simulative Models to Understand Numerical Cognition
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    Chapter 9 Restoration Model for Attenuated Low Spatial Frequencies in the Retinal Output
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    Chapter 10 Assessment and Comparison of Evolutionary Algorithms for Tuning a Bio-Inspired Retinal Model
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    Chapter 11 Efficient Localization in Edge Detection by Adapting Articial Bee Colony (ABC) Algorithm
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    Chapter 12 Electric Vehicle Charging Scheduling Using an Artificial Bee Colony Algorithm
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    Chapter 13 Protein Folding Modeling with Neural Cellular Automata Using the Face-Centered Cubic Model
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    Chapter 14 A Memetic Algorithm for Due-Date Satisfaction in Fuzzy Job Shop Scheduling
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    Chapter 15 Indicator-Based Evolutionary Level Set Approximation: Mixed Mutation Strategy and Extended Analysis
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    Chapter 16 Genetic Algorithm for Scheduling Charging Times of Electric Vehicles Subject to Time Dependent Power Availability
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    Chapter 17 Simulation of a Dynamic Prey-Predator Spatial Model Based on Cellular Automata Using the Behavior of the Metaheuristic African Buffalo Optimization
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    Chapter 18 An Innovative Tool to Create Neurofeedback Games for ADHD Treatment
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    Chapter 19 Conditional Entropy Estimates for Distress Detection with EEG Signals
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    Chapter 20 Nonlinear Symbolic Assessment of Electroencephalographic Recordings for Negative Stress Recognition
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    Chapter 21 Recent Advances and Challenges in Nonlinear Characterization of Brain Dynamics for Automatic Recognition of Emotional States
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    Chapter 22 Towards Assistive Solutions for People with Central Vision Loss
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    Chapter 23 Performance of Predicting Surface Quality Model Using Softcomputing, a Comparative Study of Results
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    Chapter 24 Temporal Dynamics of Human Emotions: An Study Combining Images and Music
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    Chapter 25 Memory Effect in Expressed Emotions During Long Term Group Interactions
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    Chapter 26 Setting the Parameters for an Accurate EEG (Electroencephalography)-Based Emotion Recognition System
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    Chapter 27 Exploring the Physiological Basis of Emotional HRI Using a BCI Interface
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    Chapter 28 Preliminary Study on Unilateral Sensorineural Hearing Loss Identification via Dual-Tree Complex Wavelet Transform and Multinomial Logistic Regression
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    Chapter 29 On a Heavy-Tailed Intensity Normalization of the Parkinson’s Progression Markers Initiative Brain Database
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    Chapter 30 Case-Based Statistical Learning: A Non Parametric Implementation Applied to SPECT Images
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    Chapter 31 PET Image Classification Using HHT-Based Features Through Fractal Sampling
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    Chapter 32 A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease
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    Chapter 33 Non-linear Covariance Estimation for Reconstructing Neural Activity with MEG/EEG Data
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    Chapter 34 Automatic Separation of Parkinsonian Patients and Control Subjects Based on the Striatal Morphology
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    Chapter 35 Emotion Assessment Based on Functional Connectivity Variability and Relevance Analysis
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    Chapter 36 MRI-Based Feature Extraction Using Supervised General Stochastic Networks in Dementia Diagnosis
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    Chapter 37 Influence of Population Dependent Forward Models on Distributed EEG Source Reconstruction
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    Chapter 38 Influence of Anisotropic Blood Vessels Modeling on EEG Source Localization
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    Chapter 39 Brain White Matter Lesion Segmentation with 2D/3D CNN
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    Chapter 40 Circadian Modulation of Sleep-Wake Dynamics Evaluated by Transition Probabilities
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    Chapter 41 EEG Source Imaging Based on Dynamic Sparse Coding as ADHD Biomarker
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    Chapter 42 Identification of Nonstationary Brain Networks Using Time-Variant Autoregressive Models
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    Chapter 43 Detection of EEG Dynamic Changes Due to Stimulus-Related Activity in Motor Imagery Recordings
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    Chapter 44 Sleep Stages Clustering Using Time and Spectral Features of EEG Signals
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    Chapter 45 Segment Clustering for Holter Recordings Analysis
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    Chapter 46 Towards a Deep Learning Model of Retina: Retinal Neural Encoding of Color Flash Patterns
Overall attention for this book and its chapters
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Natural and Artificial Computation for Biomedicine and Neuroscience
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-59740-9
978-3-31-959739-3, 978-3-31-959740-9

José Manuel Ferrández Vicente, José Ramón Álvarez-Sánchez, Félix de la Paz López, Javier Toledo Moreo, Hojjat Adeli

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 25%
Student > Ph. D. Student 7 22%
Researcher 6 19%
Professor 2 6%
Student > Bachelor 2 6%
Other 1 3%
Unknown 6 19%
Readers by discipline Count As %
Computer Science 9 28%
Engineering 8 25%
Agricultural and Biological Sciences 2 6%
Neuroscience 2 6%
Physics and Astronomy 2 6%
Other 2 6%
Unknown 7 22%