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

FindBook

Google Book

Amazon

博客來

The Evolution of Fundamental Neural Circuits for Cognition in silico.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The Evolution of Fundamental Neural Circuits for Cognition in silico./
作者:	Tehrani-Saleh, Ali.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	168 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28717997
ISBN:	9798538136780

The Evolution of Fundamental Neural Circuits for Cognition in silico.
Tehrani-Saleh, Ali.

The Evolution of Fundamental Neural Circuits for Cognition in silico. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 168 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--Michigan State University, 2021.

This item must not be sold to any third party vendors.

Despite decades of research on intelligence and fundamental components of cognition, we still know very little about the structure and functionality of nervous systems. Questions in cognition and intelligent behavior are addressed by scientists in the fields of behavioral biology, neuroscience, psychology, and computer science. Yet it is difficult to reverse engineer observed sophisticated intelligent behaviors in animals and even more difficult to understand their underlying mechanisms. In this dissertation, I use a recently-developed neuroevolution platform -called Markov brain networks - in which Darwinian selection is used to evolve both structure and functionality of digital brains. I use this platform to study some of the most fundamental cognitive neural circuits: 1) visual motion detection, 2) collision-avoidance based on visual motion cues, 3) sound localization, and 4) time perception. In particular, I investigate both the selective pressures and environmental conditions in the evolution of these cognitive components, as well as the circuitry and computations behind them. This dissertation lays the groundwork for an evolutionary agent-based method to study the neural circuits for cognition in silico.

ISBN: 9798538136780Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Artificial life

The Evolution of Fundamental Neural Circuits for Cognition in silico.
LDR:02490nmm a2200421 4500 001 2348638
005 20220912135624.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798538136780
035 $a (MiAaPQ)AAI28717997
035 $a AAI28717997
040 $a MiAaPQ $c MiAaPQ
100 1 $a Tehrani-Saleh, Ali. $3 3688008
245 1 4 $a The Evolution of Fundamental Neural Circuits for Cognition in silico.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 168 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Adami, Christoph.
502 $a Thesis (Ph.D.)--Michigan State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Despite decades of research on intelligence and fundamental components of cognition, we still know very little about the structure and functionality of nervous systems. Questions in cognition and intelligent behavior are addressed by scientists in the fields of behavioral biology, neuroscience, psychology, and computer science. Yet it is difficult to reverse engineer observed sophisticated intelligent behaviors in animals and even more difficult to understand their underlying mechanisms. In this dissertation, I use a recently-developed neuroevolution platform -called Markov brain networks - in which Darwinian selection is used to evolve both structure and functionality of digital brains. I use this platform to study some of the most fundamental cognitive neural circuits: 1) visual motion detection, 2) collision-avoidance based on visual motion cues, 3) sound localization, and 4) time perception. In particular, I investigate both the selective pressures and environmental conditions in the evolution of these cognitive components, as well as the circuitry and computations behind them. This dissertation lays the groundwork for an evolutionary agent-based method to study the neural circuits for cognition in silico.
590 $a School code: 0128.
650 4 $a Computer science. $3 523869
650 4 $a Quantitative psychology. $3 2144748
650 4 $a Psychology. $3 519075
650 4 $a Behavioral sciences. $3 529833
650 4 $a Behavioral psychology. $3 2122788
653 $a Artificial life
653 $a Cognition
653 $a Computational evolution
653 $a Evolution
653 $a Neural circuits
653 $a Nervous system
653 $a Intelligent behavior
690 $a 0984
690 $a 0632
690 $a 0621
690 $a 0602
690 $a 0384
710 2 $a Michigan State University. $b Computer Science - Doctor of Philosophy. $3 2104328
773 0 $t Dissertations Abstracts International $g 83-03B.
790 $a 0128
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28717997