| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000432806 |
| 005 | | 20200224135853 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781392301432 |
| 035 | |
▼a (MiAaPQ)AAI13877782 |
| 035 | |
▼a (MiAaPQ)umaryland:11045 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 001 |
| 100 | 1 |
▼a Pietras, Bradley William. |
| 245 | 10 |
▼a Computational Modeling of Behavior and Neural Mechanisms of Decision-Making Using Reinforcement Learning Theory. |
| 260 | |
▼a [S.l.]:
▼b University of Maryland, Baltimore.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 197 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 80-12, Section: B. |
| 500 | |
▼a Publisher info.: Dissertation/Thesis. |
| 500 | |
▼a Advisor: Schoenbaum, Geoffrey |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Maryland, Baltimore, 2019. |
| 506 | |
▼a This item must not be added to any third party search indexes. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a In the study of learning and decision-making in animals and humans, the field of Reinforcement Learning (RL) offers powerful ideas and tools for exploring the control mechanisms that underlie behavior.In this dissertation, we use RL to examine the questions of (i) how rats represent information about a complex, changing, task |
| 590 | |
▼a School code: 0373. |
| 650 | 4 |
▼a Neurosciences. |
| 650 | 4 |
▼a Artificial intelligence. |
| 690 | |
▼a 0317 |
| 690 | |
▼a 0800 |
| 710 | 20 |
▼a University of Maryland, Baltimore.
▼b Neuroscience. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 80-12B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0373 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15491078
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |