國立虎尾科技大學 |

Signal coding and reconstruction through a framework which utilizes naturally-inspired deterministic spiking models.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Signal coding and reconstruction through a framework which utilizes naturally-inspired deterministic spiking models./
作者:	Kaya, Gokhan.
面頁冊數:	1 online resource (46 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9781369599428

Signal coding and reconstruction through a framework which utilizes naturally-inspired deterministic spiking models.
Kaya, Gokhan.

Signal coding and reconstruction through a framework which utilizes naturally-inspired deterministic spiking models. - 1 online resource (46 pages)

Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.

Thesis (Ph.D.)--University of Florida, 2016.

Includes bibliographical references

Spiking is the common form of action adopted by neurons which cumulatively creates the notion of activity in the nervous system. Any attempt on defining a neural activity in the form of information processing and organization requires studying what individual spikes or spiking represents towards the definition in question. In order to define and discuss the representation abilities of spikes, the problem of deterministically coding a continuous time signal using an ensemble of spike trains is addressed. Coding is, with an eye toward "efficiency", defined as a trade-off between the number of spikes in the code and the quality of the code operationalized using the notion of reconstruction error. It is shown that inverting the coding model leads to a reconstruction procedure that amounts to a constrained optimization problem. A class of coding models is considered that makes the coding procedure biologically plausible while at the same time making the reconstruction problem tractable. It is demonstrated that the reconstruction error depends acutely on the coding model. This tight coupling is then used to describe a procedure that learns a coding model of improved efficiency. Experiments on speech data show the performance of the coding described.

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

Mode of access: World Wide Web

ISBN: 9781369599428Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Signal coding and reconstruction through a framework which utilizes naturally-inspired deterministic spiking models.
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504 $a Includes bibliographical references
520 $a Spiking is the common form of action adopted by neurons which cumulatively creates the notion of activity in the nervous system. Any attempt on defining a neural activity in the form of information processing and organization requires studying what individual spikes or spiking represents towards the definition in question. In order to define and discuss the representation abilities of spikes, the problem of deterministically coding a continuous time signal using an ensemble of spike trains is addressed. Coding is, with an eye toward "efficiency", defined as a trade-off between the number of spikes in the code and the quality of the code operationalized using the notion of reconstruction error. It is shown that inverting the coding model leads to a reconstruction procedure that amounts to a constrained optimization problem. A class of coding models is considered that makes the coding procedure biologically plausible while at the same time making the reconstruction problem tractable. It is demonstrated that the reconstruction error depends acutely on the coding model. This tight coupling is then used to describe a procedure that learns a coding model of improved efficiency. Experiments on speech data show the performance of the coding described.
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538 $a Mode of access: World Wide Web
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