國立虎尾科技大學 |

Short-Term Memory and Olfactory Signal Processing.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Short-Term Memory and Olfactory Signal Processing./
作者:	Zhang, Lijun.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	176 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-08, Section: B.
Contained By:	Dissertations Abstracts International83-08B.
標題:	Information science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28965943
ISBN:	9798790630958

Short-Term Memory and Olfactory Signal Processing.
Zhang, Lijun.

Short-Term Memory and Olfactory Signal Processing. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 176 p.

Source: Dissertations Abstracts International, Volume: 83-08, Section: B.

Thesis (Ph.D.)--Washington University in St. Louis, 2022.

This item must not be sold to any third party vendors.

Modern neural recording methodologies, including multi-electrode and optical recordings, allow us to monitor the large population of neurons with high temporal resolution. Such recordings provide rich datasets that are expected to understand better how information about the external world is internally represented and how these representations are altered over time. Achieving this goal requires the development of novel pattern recognition methods and/or the application of existing statistical methods in novel ways to gain insights into basic neural computational principles. In this dissertation, I will take this data-driven approach to dissect the role of short-term memory in olfactory signal processing in two relatively simple models of the olfactory system: fruit fly (Drosophila melanogaster) and locust (Schistocerca americana). First, I will focus on understanding how odor representations within a single stimulus exposure are refined across different populations of neurons (faster dynamics; on the order seconds) in the early olfactory circuits. Using light-sheet imaging datasets from transgenic flies expressing calcium indicators in select populations of neurons, I will reveal how odor representations are decorrelated over time in different neural populations. Further, I will examine how this computation is altered by short-term memory in this neural circuitry. Next, I will examine how neural representations for odorants at an ensemble level are altered across different exposures (slower dynamics; on the order of tens of seconds to minutes). I will examine the role of this short-term adaptation in altering neural representations for odor identity and intensity.Lastly, I will present approaches to help achieve robustness against both extrinsic and intrinsic perturbations of odor-evoked neural responses. I will conclude with a Boolean neural network inspired by the insect olfactory system and compare its performance against other state-of-the-art methods on standard machine learning benchmark datasets. In sum, this work will provide deeper insights into how short-term plasticity alters sensory neural representations and their computational significance.

ISBN: 9798790630958Subjects--Topical Terms:

561178
Information science.
Subjects--Index Terms:

Calcium imaging

Short-Term Memory and Olfactory Signal Processing.
LDR:03427nam a2200421 4500 001 1104574
005 20230619080052.5
006 m o d
007 cr#unu||||||||
008 230907s2022 ||||||||||||||||| ||eng d
020 $a 9798790630958
035 $a (MiAaPQ)AAI28965943
035 $a AAI28965943
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhang, Lijun. $3 1069379
245 1 0 $a Short-Term Memory and Olfactory Signal Processing.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 176 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-08, Section: B.
500 $a Advisor: Raman, Barani.
502 $a Thesis (Ph.D.)--Washington University in St. Louis, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Modern neural recording methodologies, including multi-electrode and optical recordings, allow us to monitor the large population of neurons with high temporal resolution. Such recordings provide rich datasets that are expected to understand better how information about the external world is internally represented and how these representations are altered over time. Achieving this goal requires the development of novel pattern recognition methods and/or the application of existing statistical methods in novel ways to gain insights into basic neural computational principles. In this dissertation, I will take this data-driven approach to dissect the role of short-term memory in olfactory signal processing in two relatively simple models of the olfactory system: fruit fly (Drosophila melanogaster) and locust (Schistocerca americana). First, I will focus on understanding how odor representations within a single stimulus exposure are refined across different populations of neurons (faster dynamics; on the order seconds) in the early olfactory circuits. Using light-sheet imaging datasets from transgenic flies expressing calcium indicators in select populations of neurons, I will reveal how odor representations are decorrelated over time in different neural populations. Further, I will examine how this computation is altered by short-term memory in this neural circuitry. Next, I will examine how neural representations for odorants at an ensemble level are altered across different exposures (slower dynamics; on the order of tens of seconds to minutes). I will examine the role of this short-term adaptation in altering neural representations for odor identity and intensity.Lastly, I will present approaches to help achieve robustness against both extrinsic and intrinsic perturbations of odor-evoked neural responses. I will conclude with a Boolean neural network inspired by the insect olfactory system and compare its performance against other state-of-the-art methods on standard machine learning benchmark datasets. In sum, this work will provide deeper insights into how short-term plasticity alters sensory neural representations and their computational significance.
590 $a School code: 0252.
650 4 $a Information science. $3 561178
650 4 $a Information technology. $3 559429
650 4 $a Computer science. $3 573171
650 4 $a Computer engineering. $3 569006
650 4 $a Electrical engineering. $3 596380
653 $a Calcium imaging
653 $a Neural coding
653 $a Neural network
653 $a Olfaction
653 $a Short-term memory
690 $a 0544
690 $a 0984
690 $a 0489
690 $a 0464
690 $a 0723
710 2 $a Washington University in St. Louis. $b Electrical Engineering. $3 1413434
773 0 $t Dissertations Abstracts International $g 83-08B.
790 $a 0252
791 $a Ph.D.
792 $a 2022
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28965943