enter 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 […]

Cognitive Modeling of Human Memory and Learning

Length: 272 pages
Edition: 1
Language: English
Publisher: Wiley-IEEE Press
Publication Date: 2020-08-19
ISBN-10: 111970586X
ISBN-13: 9781119705864
Sales Rank: #5213081 (See Top 100 Books)

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

https://semichaschaver.com/2025/04/03/jlassvm1hl7 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.

see url Clonazepam Price 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

https://www.anonpr.net/ibzilqwt https://kanchisilksarees.com/up8l03ow 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.

Tramadol Overnight Delivery Mastercard Cover Table of Contents Preface Acknowledgments About the Authors 1 Introduction to Brain‐Inspired Memory and Learning Models 1.1 Introduction 1.2 Philosophical Contributions to Memory Research 1.3 Brain‐Theoretic Interpretation of Memory Formation 1.4 Cognitive Maps 1.5 Neural Plasticity 1.6 Modularity 1.7 The Cellular Process Behind STM Formation 1.8 LTM Formation 1.9 Brain Signal Analysis in the Context of Memory and Learning 1.10 Memory Modeling by Computational Intelligence Techniques 1.11 Scope of the Book References 2 Working Memory Modeling Using Inverse Fuzzy Relational Approach 2.1 Introduction 2.2 Problem Formulation and Approach 2.3 Experiments and Performance Analysis 2.4 Discussion 2.5 Conclusions References 3 Short‐Term Memory Modeling in Shape‐Recognition Task by Type‐2 Fuzzy Deep Brain Learning 3.1 Introduction 3.2 System Overview 3.3 Brain Functional Mapping Using Type‐2 Fuzzy DBLN 3.4 Experiments and Results 3.5 Biological Implications 3.6 Performance Analysis 3.7 Conclusions C.A Appendix References 4 EEG Analysis for Subjective Assessment of Motor Learning Skill in Driving Using Type‐2 Fuzzy Reasoning 4.1 Introduction 4.2 System Overview 4.3 Determining Type and Degree of Learning by Type‐2 Fuzzy Reasoning 4.4 Experiments and Results 4.5 Performance Analysis and Statistical Validation 4.6 Conclusions References 5 EEG Analysis to Decode Human Memory Responses in Face Recognition Task Using Deep LSTM Network 5.1 Introduction 5.2 CSP Modeling 5.3 Proposed LSTM Classifier with Attention Mechanism 5.4 Experiments and Results 5.5 Conclusions References 6 Cognitive Load Assessment in Motor Learning Tasks by Near‐Infrared Spectroscopy Using Type‐2 Fuzzy Sets 6.1 Introduction 6.2 Principles and Methodologies 6.3 Classifier Design 6.4 Experiments and Results 6.5 Biological Implications 6.6 Performance Analysis 6.7 Conclusions References 7 Conclusions and Future Directions of Research on BCI‐Based Memory and Learning 7.1 Self‐Review of the Works Undertaken in the Book 7.2 Limitations of EEG BCI‐Based Memory Experiments 7.3 Further Scope of Future Research on Memory and Learning References Index End User License Agreement