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Large-Scale Neural Network Models for Distributed Working Memory in a Multiregional Cortex.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Large-Scale Neural Network Models for Distributed Working Memory in a Multiregional Cortex./
作者:	Ding, Xingyu.
面頁冊數:	1 online resource (178 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
標題:	Neurosciences. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30315142click for full text (PQDT)
ISBN:	9798379774530

Large-Scale Neural Network Models for Distributed Working Memory in a Multiregional Cortex.
Ding, Xingyu.

Large-Scale Neural Network Models for Distributed Working Memory in a Multiregional Cortex. - 1 online resource (178 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--New York University, 2023.

Includes bibliographical references

The recent development of brain connectomics has provided a quantitative description of the anatomy of the brain. However, connectivity maps are insufficient to account for the detailed physiology and behavior observed in different animal species. In this thesis, I present a series of computational studies aiming at elucidating the underlying mechanisms of working memory through large-scale neural circuit modeling. Specifically, I explore distributed working memory in the mouse and macaque brain as well as in human subjects. In Chapter 2, I present a connectome-based neural network model of the mouse brain, demonstrating that the concentration of PV interneurons across cortical areas influences memory-related activation patterns. In Chapter 3, a model is formulated for distributed working memory in the macaque cortex, revealing the significance of dopamine receptor density per neuron and an inverted U-shaped dependence on dopamine. Finally, in Chapter 4, I propose a recurrent attractor network model to explain differences in visual working memory precision, showing that the precision of working memory decoding and the size of visual fields are correlated. Taken together, these studies offer insights into the role of large-scale dynamics in understanding the mechanism of working memory, and will help interpret large-scale neurophysiology datasets and provide a valuable resource to the systems neuroscience community.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379774530Subjects--Topical Terms:

588700
Neurosciences.
Subjects--Index Terms:

ConnectomicsIndex Terms--Genre/Form:

542853
Electronic books.

Large-Scale Neural Network Models for Distributed Working Memory in a Multiregional Cortex.
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