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010 a| 2020015457;

020 a| 9781119705864q| (cloth);

020 z| 9781119705871q| (adobe pdf);

020 z| 9781119705918q| (epub);

035 a| (WaSeSS)ssj0002408824;

040 a| DLCb| engc| DLCd| WaSeSS;

042 a| pcc;

049 a| EREEa| NEHH;

050 00 a| BF371b| .G46 2020;

082 00 a| 153.1/201132| 23;

100 1 a| Ghosh, Lidia.?| UNAUTHORIZED;

245 10 a| Cognitive modeling of human memory and learningh| [electronic resource] :b| a non-invasive brain-computer interfacing approach /c| Lidia Ghosh, Artificial Intelligence Lab., Dept. of Electronics and Tele-Communication Engineering, Amit Konar, Artificial Intelligence Lab., Dept. of Electronics and Tele-Communication Engineering, Pratyusha Rakshit, Artificial Intelligence Lab., Dept. of Electronics and Tele-Communication Engineering.;

260 a| Hoboken, New Jersey :b| Wiley,c| [2020];

263 a| 2007;

300 a| pages cm;

504 a| Includes bibliographical references and index.;

506 a| Available only to authorized users.;

520 a| "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. It 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"--c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

650 0 a| Memory.=| ^A806;

650 0 a| Brain-computer interfaces.=| ^A1057127;

650 0 a| Cognitive neuroscience.=| ^A292661;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Konar, Amit.=| ^A605068;

700 1 a| Rakshit, Pratyusha.?| UNAUTHORIZED;

856 40 z| Full text available from Ebook Central - Academic Completeu| https://ebookcentral.proquest.com/lib/eastcarolina/detail.action?docID=6317471;

949 a| CLICK ON WEB ADDRESSw| ASISh| JOYNER188;

949 a| CLICK ON WEB ADDRESSw| ASISh| HSL77;

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999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5859485-2001l| HSLELECm| HSLr| Ys| Yt| HEBKu| 7/17/2022x| EBOOKz| HERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5859485-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 7/17/2022x| EBOOKz| JERESOURCE;

Cognitive modeling of human memory and learning a non-invasive brain-computer interfacing approach / Lidia Ghosh, Artificial Intelligence Lab., Dept. of Electronics and Tele-Communication Engineering, Amit Konar, Artificial Intelligence Lab., Dept. of Electronics and Tele-Communication Engineering, Pratyusha Rakshit, Artificial Intelligence Lab., Dept. of Electronics and Tele-Communication Engineering.

Author/creator	Ghosh, Lidia
Other author	Konar, Amit.
Other author	Rakshit, Pratyusha.
Format	Electronic
Publication Info	Hoboken, New Jersey : Wiley, [2020]
Description	pages cm
Supplemental Content	Full text available from Ebook Central - Academic Complete
Subjects	Memory. Brain-computer interfaces. Cognitive neuroscience.

Abstract	"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. It 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"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2020015457
ISBN	9781119705864 (cloth)
ISBN	(adobe pdf)
ISBN	(epub)

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