|
|
|
|
| LEADER |
02901nmm a2200265 u 4500 |
| 001 |
EB001905369 |
| 003 |
EBX01000000000000001068275 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
201218 ||| eng |
| 020 |
|
|
|a 9781119705925
|
| 050 |
|
4 |
|a BF371 .G46 2021 (print)
|
| 050 |
|
4 |
|a BF371 (ebook)
|
| 100 |
1 |
|
|a Ghosh, Lidia
|
| 245 |
0 |
0 |
|a Cognitive Modeling of Human Memory and Learning
|h Elektronische Ressource
|b A Non-invasive Brain-Computer Interfacing Approach
|c Lidia Ghosh, Amit Konar, and Pratyusha Rakshit
|
| 260 |
|
|
|a Hoboken, NJ
|b Wiley
|b IEEE Press
|c 2020, ©2021
|
| 700 |
1 |
|
|a Konar, Amit
|
| 700 |
1 |
|
|a Rakshit, Pratyusha
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b WILOB
|a Wiley Online Books
|
| 028 |
5 |
0 |
|a 10.1002/9781119705925
|
| 776 |
|
|
|z 9781119705864
|
| 856 |
4 |
0 |
|u https://doi.org/10.1002/9781119705925
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 153.1/20113–dc23
|
| 520 |
3 |
|
|a 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 ...
|