LEADER 03403nam 2200493 450 001 9910686774603321 005 20230730235957.0 010 $a9789819902798$b(electronic bk.) 010 $z9789819902781 024 7 $a10.1007/978-981-99-0279-8 035 $a(MiAaPQ)EBC7231633 035 $a(Au-PeEL)EBL7231633 035 $a(OCoLC)1374521597 035 $a(DE-He213)978-981-99-0279-8 035 $a(PPN)269094725 035 $a(EXLCZ)9926388085400041 100 $a20230730d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep cognitive networks $eenhance deep learning by modeling human cognitive mechanism /$fYan Huang and Liang Wang 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer,$d[2023] 210 4$dİ2023 215 $a1 online resource (70 pages) 225 1 $aSpringerBriefs in Computer Science,$x2191-5776 311 08$aPrint version: Huang, Yan Deep Cognitive Networks Singapore : Springer,c2023 9789819902781 320 $aIncludes bibliographical references. 327 $aChapter 1. Introduction -- Chapter 2. General Framework -- Chapter 3. Attention-based DCNs -- Chapter 4. Memory-based DCNs -- Chapter 5. Reasoning-based DCNs -- Chapter 6. Decision-based DCNs -- Chapter 7. Conclusions and Future Trends. . 330 $aAlthough deep learning models have achieved great progress in vision, speech, language, planning, control, and many other areas, there still exists a large performance gap between deep learning models and the human cognitive system. Many researchers argue that one of the major reasons accounting for the performance gap is that deep learning models and the human cognitive system process visual information in very different ways. To mimic the performance gap, since 2014, there has been a trend to model various cognitive mechanisms from cognitive neuroscience, e.g., attention, memory, reasoning, and decision, based on deep learning models. This book unifies these new kinds of deep learning models and calls them deep cognitive networks, which model various human cognitive mechanisms based on deep learning models. As a result, various cognitive functions are implemented, e.g., selective extraction, knowledge reuse, and problem solving, for more effective information processing. This book first summarizes existing evidence of human cognitive mechanism modeling from cognitive psychology and proposes a general framework of deep cognitive networks that jointly considers multiple cognitive mechanisms. Then, it analyzes related works and focuses primarily but not exclusively, on the taxonomy of four key cognitive mechanisms (i.e., attention, memory, reasoning, and decision) surrounding deep cognitive networks. Finally, this book studies two representative cases of applying deep cognitive networks to the task of image-text matching and discusses important future directions. 410 0$aSpringerBriefs in Computer Science,$x2191-5776 606 $aDeep learning (Machine learning) 615 0$aDeep learning (Machine learning) 676 $a733 700 $aHuang$b Yan$f1933-$01378574 702 $aWang$b Liang 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910686774603321 996 $aDeep cognitive networks$93417220 997 $aUNINA