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A Mathematical Theory of Synaptic Information Storage Capacity.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	A Mathematical Theory of Synaptic Information Storage Capacity./
作者:	Samavat, Mohammad.
面頁冊數:	1 online resource (122 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:	click for full text (PQDT)
ISBN:	9798379762063

A Mathematical Theory of Synaptic Information Storage Capacity.
Samavat, Mohammad.

A Mathematical Theory of Synaptic Information Storage Capacity. - 1 online resource (122 pages)

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

Thesis (Ph.D.)--University of California, San Diego, 2023.

Includes bibliographical references

Brain Connectomics is generating an ever-increasing deluge of data, which challenges us to develop new methods for analyzing and extracting new insights from these data. Connectomic researchers have focused on connectivity -- the pattern of connectivity between neurons. The strengths of synapses have also been studied by quantifying the sizes of synapses which can be regulated by learning. During my PhD, I have been developing computational methods for relating brain structure to function. I have developed tools and algorithms for analyzing connectomics data sets using Machine learning, statistical inference and Information Theory to find measures and biomarkers that can be used to probe mechanisms underlying leaning and memory in normal and diseased brains. We introduce here a powerful method for analyzing three-dimensional reconstruction from serial section electron microscopy (3DEM) to measure synaptic information storage capacity (SISC) and apply it to data following in vivo long-term potentiation (LTP). Quantifying precision is fundamental to understanding information storage and retrieval in neural circuits. We quantify this precision with Shannon information theory, which is a more reliable estimate than prior analyses based on signal detection theory. Spine head volumes are well correlated with other measures of synaptic weight, thus SISC can be determined by identifying the non-overlapping clusters of dendritic spine head volumes to determine the number of distinguishable synaptic weights. SISC analysis of spine head volumes in the stratum radiatum of hippocampal area CA1 revealed 24 distinguishable states (4.1 bits). In contrast, spine head volumes in the middle molecular layer of control dentate gyrus occupied only 5 distinguishable states (2 bits). Thus, synapses in different hippocampal regions had significantly different SISCs. Moreover, these were not fixed properties but increased by 30 min following induction of LTP in the dentate gyrus to occupy 10 distinguishable states (3 bits), and this increase lasted for at least 2 hours. We also observed a broader and nearly uniform distribution of spine head volumes across the increased number of states, suggesting the distribution evolved towards the theoretical upper bound of SISC following LTP. For dentate granule cells these findings show that the spine size range was broadened by the interplay among synaptic plasticity mechanisms. SISC provides a new analytical measure to probe these mechanisms in normal and diseased brains.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379762063Subjects--Topical Terms:

593561
Neurosciences.
Subjects--Index Terms:

ClusteringIndex Terms--Genre/Form:

554714
Electronic books.

A Mathematical Theory of Synaptic Information Storage Capacity.
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