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

Path Integral Techniques for Estimat...

Knowlton, Christopher J.

FindBook

Google Book

Amazon

博客來

Path Integral Techniques for Estimating Neural Network Connectivity.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Path Integral Techniques for Estimating Neural Network Connectivity./
作者:	Knowlton, Christopher J.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2014,
面頁冊數:	141 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-05(E), Section: B.
Contained By:	Dissertation Abstracts International76-05B(E).
標題:	Biophysics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3666863
ISBN:	9781321401745

Path Integral Techniques for Estimating Neural Network Connectivity.
Knowlton, Christopher J.

Path Integral Techniques for Estimating Neural Network Connectivity. - Ann Arbor : ProQuest Dissertations & Theses, 2014 - 141 p.

Source: Dissertation Abstracts International, Volume: 76-05(E), Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2014.

Characterizing the behavior of networks of neurons requires accounting for the differing levels of measurements at different scales. At the single neuron level, intracellular recordings allow for highly accurate membrane potential measurements in response to an designed applied current. Because the probes used for the single neuron experiments are large compared to the cells themselves, these voltage measurements cannot be assumed to be available for any more than a few cells at a time. Instead of voltage measurements of the potential across the cell membrane, extracellular voltage measurements combined with spike sorting algorithms allow for measurements of spike times on orders of magnitude more neurons. This spike timing information provides much less information per neuron, requiring the development of new methods to estimate the states and connectivity of a network of neurons.

ISBN: 9781321401745Subjects--Topical Terms:

518360
Biophysics.

Path Integral Techniques for Estimating Neural Network Connectivity.
LDR:02329nmm a2200325 4500 001 2159240
005 20180622095237.5
008 190424s2014 ||||||||||||||||| ||eng d
020 $a 9781321401745
035 $a (MiAaPQ)AAI3666863
035 $a (MiAaPQ)ucsd:14500
035 $a AAI3666863
040 $a MiAaPQ $c MiAaPQ
100 1 $a Knowlton, Christopher J. $3 3347107
245 1 0 $a Path Integral Techniques for Estimating Neural Network Connectivity.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2014
300 $a 141 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-05(E), Section: B.
500 $a Adviser: Henry D.I. Abarbanel.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2014.
520 $a Characterizing the behavior of networks of neurons requires accounting for the differing levels of measurements at different scales. At the single neuron level, intracellular recordings allow for highly accurate membrane potential measurements in response to an designed applied current. Because the probes used for the single neuron experiments are large compared to the cells themselves, these voltage measurements cannot be assumed to be available for any more than a few cells at a time. Instead of voltage measurements of the potential across the cell membrane, extracellular voltage measurements combined with spike sorting algorithms allow for measurements of spike times on orders of magnitude more neurons. This spike timing information provides much less information per neuron, requiring the development of new methods to estimate the states and connectivity of a network of neurons.
520 $a Previous work{biocyb1,biocyb2,cdm1} has demonstrated the ability of a path integral formulation to characterize the behavior of individual neurons given time series voltage data. We expand on this to potential future experiments to characterize the behavior of synaptic connections, and other external currents acting on neurons and two possible means for determining the connectivity of a network of neurons given spike timing information.
590 $a School code: 0033.
650 4 $a Biophysics. $3 518360
650 4 $a Neurosciences. $3 588700
650 4 $a Applied mathematics. $3 2122814
690 $a 0786
690 $a 0317
690 $a 0364
710 2 $a University of California, San Diego. $b Physics. $3 1035951
773 0 $t Dissertation Abstracts International $g 76-05B(E).
790 $a 0033
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3666863