國立虎尾科技大學 |

Path Integral Techniques for Estimating Neural Network Connectivity.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Path Integral Techniques for Estimating Neural Network Connectivity./
作者:	Knowlton, Christopher J.
面頁冊數:	1 online resource (141 pages)
附註:	Source: Dissertation Abstracts International, Volume: 76-05(E), Section: B.
標題:	Biophysics. -
電子資源:	click for full text (PQDT)
ISBN:	9781321401745

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

Path Integral Techniques for Estimating Neural Network Connectivity. - 1 online resource (141 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781321401745Subjects--Topical Terms:

581576
Biophysics.
Index Terms--Genre/Form:

554714
Electronic books.

Path Integral Techniques for Estimating Neural Network Connectivity.
LDR:02542ntm a2200349K 4500 001 913757
005 20180622095237.5
006 m o u
007 cr mn||||a|a||
008 190606s2014 xx obm 000 0 eng d
020 $a 9781321401745
035 $a (MiAaPQ)AAI3666863
035 $a (MiAaPQ)ucsd:14500
035 $a AAI3666863
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Knowlton, Christopher J. $3 1186723
245 1 0 $a Path Integral Techniques for Estimating Neural Network Connectivity.
264 0 $c 2014
300 $a 1 online resource (141 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: 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.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Biophysics. $3 581576
650 4 $a Neurosciences. $3 593561
650 4 $a Applied mathematics. $3 1069907
655 7 $a Electronic books. $2 local $3 554714
690 $a 0786
690 $a 0317
690 $a 0364
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, San Diego. $b Physics. $3 1180745
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3666863 $z click for full text (PQDT)