國立虎尾科技大學 |

Firing rate models elucidate competitive gamma mechanisms in the hippocampus.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Firing rate models elucidate competitive gamma mechanisms in the hippocampus./
作者:	Keeley, Stephen.
面頁冊數:	1 online resource (136 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.
Contained By:	Dissertation Abstracts International78-05B(E).
標題:	Neurosciences. -
電子資源:	click for full text (PQDT)
ISBN:	9781369332001

Firing rate models elucidate competitive gamma mechanisms in the hippocampus.
Keeley, Stephen.

Firing rate models elucidate competitive gamma mechanisms in the hippocampus. - 1 online resource (136 pages)

Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Oscillatory activity in the gamma band is a ubiquitous dynamical feature observed in an array of brain regions of mammals during both waking and sleeping states. Much existing mechanistic treatment of gamma oscillations are modeled with networks of many individual neuronal units connected via synaptic processes. These network spiking models of the gamma oscillation are limited in their ability to both communicate general and tractable network-level dynamic properties, and make predictions about the relationship between more complicated oscillatory processes and the neurons that underlie them.

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

Mode of access: World Wide Web

ISBN: 9781369332001Subjects--Topical Terms:

593561
Neurosciences.
Index Terms--Genre/Form:

554714
Electronic books.

Firing rate models elucidate competitive gamma mechanisms in the hippocampus.
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245 1 0 $a Firing rate models elucidate competitive gamma mechanisms in the hippocampus.
264 0 $c 2016
300 $a 1 online resource (136 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.
500 $a Advisers: John Rinzel; Andre A. Fenton.
502 $a Thesis (Ph.D.) $c New York University $d 2016.
504 $a Includes bibliographical references
520 $a Oscillatory activity in the gamma band is a ubiquitous dynamical feature observed in an array of brain regions of mammals during both waking and sleeping states. Much existing mechanistic treatment of gamma oscillations are modeled with networks of many individual neuronal units connected via synaptic processes. These network spiking models of the gamma oscillation are limited in their ability to both communicate general and tractable network-level dynamic properties, and make predictions about the relationship between more complicated oscillatory processes and the neurons that underlie them.
520 $a In the first part of this work I develop a rate model to capture both of the two major existing mechanisms of gamma oscillations: Excitatory-Inhibitory (E-I) based, and Inhibitory-Inhibitory (I-I) based. The models are formulated using synaptic and rate variables and capture the qualitative phenomena of the existing E-I and I-I gamma spiking models, while additionally allow for phase-plane and bifurcation analysis.
520 $a In the second part of this work, I apply the rate model to the rodent hippocampus, studying the ways multiple interneuron subtypes may impact gamma oscillations. Here, I show that models of networks with competing interneuron populations with different post-synaptic effects are sufficient to generate, within CA1, distinct oscillatory regimes. I note that strong mutual inhibition between the interneuron populations permits bistability between distinct fast and slow gamma states, whereas weak mutual inhibition generates mixed gamma states. I reinforce these concepts with basic spiking models.
520 $a In the final part of this work, I use existing experimental data to test the predictions of my model concerning fast or slow gamma oscillations in the hippocampus. In particular, I attempt to identify if distinct classes with a preference to fire during a particular gamma event, also phase-lock their spikes to the gamma rhythm during that event. I find that using phase-locking measurements with a nearby local field potential, I can identify interneurons that distinctly lock to fast and slow gamma states, but these interneuron classes do not necessarily correspond with increased firing rates during gamma events. I finally discuss the implications of these findings.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Neurosciences. $3 593561
650 4 $a Applied mathematics. $3 1069907
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0364
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a New York University. $b Center for Neural Science. $3 1180547
773 0 $t Dissertation Abstracts International $g 78-05B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10192341 $z click for full text (PQDT)