東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Building and Evaluating Computationa...

Kong, Nathan C.L.,

FindBook

Google Book

Amazon

博客來

Building and Evaluating Computational Models of the Mammalian Visual System /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Building and Evaluating Computational Models of the Mammalian Visual System // Nathan C.L Kong.
作者:	Kong, Nathan C.L.,
面頁冊數:	1 electronic resource (168 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
標題:	Euclidean space. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30614610
ISBN:	9798380481595

Building and Evaluating Computational Models of the Mammalian Visual System /
Kong, Nathan C.L.,

Building and Evaluating Computational Models of the Mammalian Visual System /Nathan C.L Kong. - 1 electronic resource (168 pages)

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

Animals continuously and dynamically process sensory information in service of both flexible and inflexible behaviours. To understand the brain's complex information-processing pipeline by which such behaviours arise, we must first understand how the brain transforms sensory information from its raw form. This will then allow us determine what information is accessible downstream in the process. In this dissertation, we try to understand how the brain processes visual information, which entails building and evaluating computational models that can predict how the animal will respond to novel visual inputs. We focus on a class of models known as convolutional neural networks (CNNs) and demonstrate ways in which they can be evaluated against and be built for primates and for rodents to better understand how the mammalian visual system supports behaviour. We first demonstrate a time-resolved correspondence between a feedforward CNN and whole-brain neural responses during human object processing and develop a data-driven optimization approach to improve upon correlations achieved between the model and the neural data. Motivated by extensive empirical work in rodents on navigational and on decision-making behaviours and by the desire to integrate models of cortical and of subcortical areas that support these behaviours, we build quantitatively accurate CNN models of the mouse visual system. Although CNNs are state-of-the-art models of primate and of rodent visual processing, they are extremely brittle. We therefore examine the nature of their brittleness and show the existence of representational di↵erences between primary visual cortex of non-human primates and the models. Finally, we suggest that building lessbrittle models will require us to incorporate the temporally-continuous nature of the visual inputs that animals receive. Looking forward, we hope that models of sensory cortex can be integrated with computational models of downstream cortical and subcortical areas, so that we can better understand how flexible and inflexible behaviours arise.

English

ISBN: 9798380481595Subjects--Topical Terms:

3562319
Euclidean space.

Building and Evaluating Computational Models of the Mammalian Visual System /
LDR:03404nmm a22003733i 4500 001 2400448
005 20250522084128.5
006 m o d
007 cr|nu||||||||
008 251215s2023 miu||||||m |||||||eng d
020 $a 9798380481595
035 $a (MiAaPQD)AAI30614610
035 $a (MiAaPQD)STANFORDqk198bh1136
035 $a AAI30614610
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Kong, Nathan C.L., $e author. $3 3770447
245 1 0 $a Building and Evaluating Computational Models of the Mammalian Visual System / $c Nathan C.L Kong.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 1 electronic resource (168 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
500 $a Advisors: Norcia, Anthony Committee members: Gardner, Justin; Grill-Spector, Kalanit; Yamins, Dan; Bent, Stacey F.
502 $b Ph.D. $c Stanford University $d 2023.
520 $a Animals continuously and dynamically process sensory information in service of both flexible and inflexible behaviours. To understand the brain's complex information-processing pipeline by which such behaviours arise, we must first understand how the brain transforms sensory information from its raw form. This will then allow us determine what information is accessible downstream in the process. In this dissertation, we try to understand how the brain processes visual information, which entails building and evaluating computational models that can predict how the animal will respond to novel visual inputs. We focus on a class of models known as convolutional neural networks (CNNs) and demonstrate ways in which they can be evaluated against and be built for primates and for rodents to better understand how the mammalian visual system supports behaviour. We first demonstrate a time-resolved correspondence between a feedforward CNN and whole-brain neural responses during human object processing and develop a data-driven optimization approach to improve upon correlations achieved between the model and the neural data. Motivated by extensive empirical work in rodents on navigational and on decision-making behaviours and by the desire to integrate models of cortical and of subcortical areas that support these behaviours, we build quantitatively accurate CNN models of the mouse visual system. Although CNNs are state-of-the-art models of primate and of rodent visual processing, they are extremely brittle. We therefore examine the nature of their brittleness and show the existence of representational di↵erences between primary visual cortex of non-human primates and the models. Finally, we suggest that building lessbrittle models will require us to incorporate the temporally-continuous nature of the visual inputs that animals receive. Looking forward, we hope that models of sensory cortex can be integrated with computational models of downstream cortical and subcortical areas, so that we can better understand how flexible and inflexible behaviours arise.
546 $a English
590 $a School code: 0212
650 4 $a Euclidean space. $3 3562319
650 4 $a Emotions. $3 524569
650 4 $a Neural networks. $3 677449
650 4 $a Mathematics. $3 515831
690 $a 0800
690 $a 0405
710 2 $a Stanford University. $e degree granting institution. $3 3765820
720 1 $a Norcia, Anthony $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-04B.
790 $a 0212
791 $a Ph.D.
792 $a 2023
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30614610