東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back to Search results for [ null ]

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Biologically True Event-Based Spiking Neural Network Algorithm and Simulation of Tritonia diomedea Escape Swim.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Biologically True Event-Based Spiking Neural Network Algorithm and Simulation of Tritonia diomedea Escape Swim./
Author:	Miri, Fatemehossadat.
Description:	1 online resource (174 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
Subject:	Systems science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29169772click for full text (PQDT)
ISBN:	9798834064770

Biologically True Event-Based Spiking Neural Network Algorithm and Simulation of Tritonia diomedea Escape Swim.
Miri, Fatemehossadat.

Biologically True Event-Based Spiking Neural Network Algorithm and Simulation of Tritonia diomedea Escape Swim. - 1 online resource (174 pages)

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

Thesis (Ph.D.)--State University of New York at Binghamton, 2022.

Includes bibliographical references

The dynamics of the Spiking Neural Network (SNN) are most suitable to be modeled by an event-based logic. However, some biological features that are preserved in the third generation of Artificial Neural Networks (ANN) make the event-based simulation of an SNN a non-trivial task. Researchers in the field either adapt a time-based logic to implement the SNN algorithm or use an event-based logic but eliminate some biological details to overcome the complexity. These would include elements such as the synaptic delay or the membrane potential properties, which occur in the case of an action potential. These biological details are important aspects of SNN asthey are the differential components over the previous ANN generations. In this study, a novel event-based SNN algorithm is proposed. It preserves the biological aspects of neurons and synapses to a higher degree and provides control over a unique combination of parameters. The methodology operates based on accurate spike occurrence and exact spike time. Based on the proposed algorithm, a simulator named Synapse was developed in Java. Synapse was used to artificially reconstruct the neuronal dynamics of the Tritonia Diomedea escape swim to validate the algorithm. Simulated patterns from this research match the spike patterns that have been recorded from Tritonia's brain. Results also match the few existing artificially generated signals from other studies. This research is the first to simulate the complete network of the Tritonia escape swim. In addition to the central pattern generator components, the network includes the sensory, trigger, ramp, and flexion neurons and their synaptic connections. Results show that it is feasible to simulate SNN in an event-based fashion while honoring SNN's biological inspirations. Also,the proposed approach show potential as a tool to study isolated biological happenings.

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

Mode of access: World Wide Web

ISBN: 9798834064770Subjects--Topical Terms:

3168411
Systems science.
Subjects--Index Terms:

Artificial intelligenceIndex Terms--Genre/Form:

542853
Electronic books.

Biologically True Event-Based Spiking Neural Network Algorithm and Simulation of Tritonia diomedea Escape Swim.
LDR:03360nmm a2200397K 4500 001 2354856
005 20230505090441.5
006 m o d
007 cr mn ---uuuuu
008 241011s2022 xx obm 000 0 eng d
020 $a 9798834064770
035 $a (MiAaPQ)AAI29169772
035 $a AAI29169772
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Miri, Fatemehossadat. $3 3695230
245 1 0 $a Biologically True Event-Based Spiking Neural Network Algorithm and Simulation of Tritonia diomedea Escape Swim.
264 0 $c 2022
300 $a 1 online resource (174 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
500 $a Advisor: Lewis, Harold W., III.
502 $a Thesis (Ph.D.)--State University of New York at Binghamton, 2022.
504 $a Includes bibliographical references
520 $a The dynamics of the Spiking Neural Network (SNN) are most suitable to be modeled by an event-based logic. However, some biological features that are preserved in the third generation of Artificial Neural Networks (ANN) make the event-based simulation of an SNN a non-trivial task. Researchers in the field either adapt a time-based logic to implement the SNN algorithm or use an event-based logic but eliminate some biological details to overcome the complexity. These would include elements such as the synaptic delay or the membrane potential properties, which occur in the case of an action potential. These biological details are important aspects of SNN asthey are the differential components over the previous ANN generations. In this study, a novel event-based SNN algorithm is proposed. It preserves the biological aspects of neurons and synapses to a higher degree and provides control over a unique combination of parameters. The methodology operates based on accurate spike occurrence and exact spike time. Based on the proposed algorithm, a simulator named Synapse was developed in Java. Synapse was used to artificially reconstruct the neuronal dynamics of the Tritonia Diomedea escape swim to validate the algorithm. Simulated patterns from this research match the spike patterns that have been recorded from Tritonia's brain. Results also match the few existing artificially generated signals from other studies. This research is the first to simulate the complete network of the Tritonia escape swim. In addition to the central pattern generator components, the network includes the sensory, trigger, ramp, and flexion neurons and their synaptic connections. Results show that it is feasible to simulate SNN in an event-based fashion while honoring SNN's biological inspirations. Also,the proposed approach show potential as a tool to study isolated biological happenings.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Systems science. $3 3168411
650 4 $a Aquatic sciences. $3 3174300
653 $a Artificial intelligence
653 $a Event-based simulation
653 $a Spike response model
653 $a Spiking neural network
653 $a Tritonia diomedea escape swim
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0790
690 $a 0800
690 $a 0792
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a State University of New York at Binghamton. $b Systems Science Industrial Engineering. $3 2104041
773 0 $t Dissertations Abstracts International $g 84-02B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29169772 $z click for full text (PQDT)