語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
到查詢結果
[ author_sort:"gunay, cengiz." ]
切換:
標籤
|
MARC模式
|
ISBD
Hierarchical learning of conjunctive...
~
Gunay, Cengiz.
FindBook
Google Book
Amazon
博客來
Hierarchical learning of conjunctive concepts in spiking neural networks.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Hierarchical learning of conjunctive concepts in spiking neural networks./
作者:
Gunay, Cengiz.
面頁冊數:
223 p.
附註:
Source: Dissertation Abstracts International, Volume: 64-12, Section: B, page: 6162.
Contained By:
Dissertation Abstracts International64-12B.
標題:
Computer Science. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3116874
ISBN:
0496643078
Hierarchical learning of conjunctive concepts in spiking neural networks.
Gunay, Cengiz.
Hierarchical learning of conjunctive concepts in spiking neural networks.
- 223 p.
Source: Dissertation Abstracts International, Volume: 64-12, Section: B, page: 6162.
Thesis (Ph.D.)--University of Louisiana at Lafayette, 2003.
The temporal correlation hypothesis proposes that synchronous activity in different regions of the brain describes integral entities. This temporal binding approach is a possible solution to the longstanding binding problem of representing composite objects. To complement the dynamic nature of temporal binding, a recruitment learning method has been proposed for providing long-term storage. We improve the recruitment method to use a more realistic and powerful spiking neuron model.
ISBN: 0496643078Subjects--Topical Terms:
626642
Computer Science.
Hierarchical learning of conjunctive concepts in spiking neural networks.
LDR
:01817nmm 2200301 4500
001
1838416
005
20050526083751.5
008
130614s2003 eng d
020
$a
0496643078
035
$a
(UnM)AAI3116874
035
$a
AAI3116874
040
$a
UnM
$c
UnM
100
1
$a
Gunay, Cengiz.
$3
1926834
245
1 0
$a
Hierarchical learning of conjunctive concepts in spiking neural networks.
300
$a
223 p.
500
$a
Source: Dissertation Abstracts International, Volume: 64-12, Section: B, page: 6162.
500
$a
Director: Anthony S. Maida.
502
$a
Thesis (Ph.D.)--University of Louisiana at Lafayette, 2003.
520
$a
The temporal correlation hypothesis proposes that synchronous activity in different regions of the brain describes integral entities. This temporal binding approach is a possible solution to the longstanding binding problem of representing composite objects. To complement the dynamic nature of temporal binding, a recruitment learning method has been proposed for providing long-term storage. We improve the recruitment method to use a more realistic and powerful spiking neuron model.
520
$a
However, using continuous-time spiking neurons and brain-like connectivity assumptions poses new problems in hierarchical recruitment. First, we propose timing parameter constraints for recruitment over asymmetrically delayed lines. Second, we calculate required feedforward excitatory and lateral inhibitory connection densities for stable propagation of activity independent of network size.
590
$a
School code: 1363.
650
4
$a
Computer Science.
$3
626642
650
4
$a
Biology, Neuroscience.
$3
1017680
650
4
$a
Artificial Intelligence.
$3
769149
690
$a
0984
690
$a
0317
690
$a
0800
710
2 0
$a
University of Louisiana at Lafayette.
$3
1025962
773
0
$t
Dissertation Abstracts International
$g
64-12B.
790
1 0
$a
Maida, Anthony S.,
$e
advisor
790
$a
1363
791
$a
Ph.D.
792
$a
2003
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3116874
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9187930
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入