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Firing rate models elucidate competi...
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Keeley, Stephen.
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Firing rate models elucidate competitive gamma mechanisms in the hippocampus.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Firing rate models elucidate competitive gamma mechanisms in the hippocampus./
作者:
Keeley, Stephen.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:
136 p.
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:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10192341
ISBN:
9781369332001
Firing rate models elucidate competitive gamma mechanisms in the hippocampus.
Keeley, Stephen.
Firing rate models elucidate competitive gamma mechanisms in the hippocampus.
- Ann Arbor : ProQuest Dissertations & Theses, 2016 - 136 p.
Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.
Thesis (Ph.D.)--New York University, 2016.
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.
ISBN: 9781369332001Subjects--Topical Terms:
588700
Neurosciences.
Firing rate models elucidate competitive gamma mechanisms in the hippocampus.
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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.
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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.
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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.
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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.
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