Lidia Ghosh, Amit Konar, Pratyusha Rakshit

Cognitive Modeling of Human Memory and Learning

A Non-Invasive Brain-Computer Interfacing Approach

• Gebundenes Buch

Produktbeschreibung

Proposes computational models of human memory and learning using a brain-computer interfacing (BCI) approach

Human memory modeling is important from two perspectives. First, the precise fitting of the model to an individual's short-term or working memory may help in predicting memory performance of the subject in future. Second, memory models provide a biological insight to the encoding and recall mechanisms undertaken by the neurons present in active brain lobes, participating in the memorization process. This book models human memory from a cognitive standpoint by utilizing brain activations acquired from the cortex by electroencephalographic (EEG) and functional near-infrared-spectroscopic (f-NIRs) means.

Cognitive Modeling of Human Memory and Learning A Non-invasive Brain-Computer Interfacing Approach begins with an overview of the early models of memory. The authors then propose a simplistic model of Working Memory (WM) built with fuzzy Hebbian learning. A second perspective of memory models is concerned with Short-Term Memory (STM)-modeling in the context of 2-dimensional object-shape reconstruction from visually examined memorized instances. A third model assesses the subjective motor learning skill in driving from erroneous motor actions. Other models introduce a novel strategy of designing a two-layered deep Long Short-Term Memory (LSTM) classifier network and also deal with cognitive load assessment in motor learning tasks associated with driving. The book ends with concluding remarks based on principles and experimental results acquired in previous chapters.
_ Examines the scope of computational models of memory and learning with special emphasis on classification of memory tasks by deep learning-based models
_ Proposes two algorithms of type-2 fuzzy reasoning: Interval Type-2 fuzzy reasoning (IT2FR) and General Type-2 Fuzzy Sets (GT2FS)
_ Considers three classes of cognitive loads in the motor learning tasks for driving learners

Cognitive Modeling of Human Memory and Learning A Non-invasive Brain-Computer Interfacing Approach will appeal to researchers in cognitive neuro-science and human/brain-computer interfaces. It is also beneficial to graduate students of computer science/electrical/electronic engineering.

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Produktdetails

• IEEE Press
• Verlag: Wiley / Wiley & Sons
• Artikelnr. des Verlages: 1W119705860
• 1. Auflage
• Seitenzahl: 272
• Erscheinungstermin: 6. Oktober 2020
• Englisch
• Abmessung: 231mm x 150mm x 23mm
• Gewicht: 496g
• ISBN-13: 9781119705864
• ISBN-10: 111970586X
• Artikelnr.: 58778609

Autorenporträt

LIDIA GHOSH, PHD, is currently a post-doctoral research fellow on Brain Science and Memory Research, granted by Liverpool Hope University to Jadavpur University, India. AMIT KONAR, PHD, is currently a Professor in the dept. of Electronics and Tele-Communication Engineering (ETCE), Jadavpur University. He is an author of 15 books including a Wiley title: Emotion Recognition-A Pattern Analysis Approach. PRATYUSHA RAKSHIT, PHD, is an Assistant Professor of ETCE dept., Jadavpur University, India and is currently on lien to Basque Centre for Applied Mathematics, Bilbao, Spain.

Inhaltsangabe

Preface xi

Acknowledgments xvii

About the Authors xix

1 Introduction to Brain-Inspired Memory and Learning Models 1

1.1 Introduction 1

1.2 Philosophical Contributions to Memory Research 3

1.2.1 Atkinson and Shiffrin's Model 4

1.2.2 Tveter's Model 5

1.2.3 Tulving's Model 6

1.2.4 The Parallel and Distributed Processing (PDP) Approach 6

1.2.5 Procedural and Declarative Memory 8

1.3 Brain-Theoretic Interpretation of Memory Formation 10

1.3.1 Coding for Memory 10

1.3.2 Memory Consolidation 12

1.3.3 Location of Stored Memories 14

1.3.4 Isolation of Information in Memory 15

1.4 Cognitive Maps 16

1.5 Neural Plasticity 17

1.6 Modularity 18

1.7 The Cellular Process Behind STM Formation 18

1.8 LTM Formation 20

1.9 Brain Signal Analysis in the Context of Memory and Learning 20

1.9.1 Association of EEG alpha and theta Band with Memory Performances 21

1.9.2 Oscillatory ß and gamma Frequency Band Activation in STM Performance 24

1.9.3 Change in EEG Band Power with Changing Working Memory Load 24

1.9.4 Effects of Electromagnetic Field on the EEG Response of Working Memory 27

1.9.5 EEG Analysis to Discriminate Focused Attention and WM Performance 28

1.9.6 EEG Power Changes in Memory Repetition Effect 29

1.9.7 Correlation Between LTM Retrieval and EEG Features 32

1.9.8 Impact of Math Anxiety on WM Response: An EEG Study 34

1.10 Memory Modeling by Computational Intelligence Techniques 35

1.11 Scope of the Book 39

References 43

2 Working Memory Modeling Using Inverse Fuzzy Relational Approach 51

2.1 Introduction 52

2.2 Problem Formulation and Approach 54

2.2.1 Independent Component Analysis as a Source Localization Tool 55

2.2.2 Independent Component Analysis vs. Principal Component Analysis 58

2.2.3 Feature Extraction 58

2.2.4 Phase 1: WM Modeling 59

2.2.4.1 Step I: WM Modeling of Subject Using EEG Signals During Full Face Encoding and Recall from Specific Part of Same Face 60

2.2.4.2 Step II: WM Modeling of Subject Using EEG Signals During Full Face Encoding and Recall from All Parts of Same Face 62

2.2.5 Phase 2: WM Analysis 62

2.2.6 Finding Max-Min Compositional Inverse of Weight Matrix W¯ck 65

2.3 Experiments and Performance Analysis 70

2.3.1 Experimental Set-up 71

2.3.2 Source Localization Using eLORETA 73

2.3.3 Pre-processing 74

2.3.4 Selection of EEG Features 74

2.3.5 WM Model Consistency Across Partial Face Stimuli 77

2.3.6 Inter-person Variability of W 77

2.3.7 Variation in Imaging Attributes 77

2.3.8 Comparative Analysis with Existing Fuzzy Inverse Relations 84

2.4 Discussion 85

2.5 Conclusions 86

References 88

3 Short-Term Memory Modeling in Shape-Recognition Task by Type-2 Fuzzy Deep Brain Learning 93

3.1 Introduction 94

3.2 System Overview 96

3.3 Brain Functional Mapping Using Type-2 Fuzzy DBLN 101

3.3.1 Overview of Type-2 Fuzzy Sets 103

3.3.2 Type-2 Fuzzy Mapping and Parameter Adaptation by Perceptron-Like Learning 104

3.3.2.1 Construction of the Proposed Interval Type-2 Fuzzy Membership Function (IT2MF) 104

3.3.2.2 Construction of IT2FS-Induced Mapping Function 105

3.3.2.3 Secondary Membership Function Computation of Proposed GT2FS 107

3.3.2.4 Proposed General Type-2 Fuzzy Mapping 108

3.3.