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Synaptic Neural Networks--Supervised Learning Without Weights

Permanent Link: http://ncf.sobek.ufl.edu/NCFE004063/00001

Material Information

Title: Synaptic Neural Networks--Supervised Learning Without Weights
Physical Description: Book
Language: English
Creator: Caswell, Chris
Publisher: New College of Florida
Place of Publication: Sarasota, Fla.
Creation Date: 2009
Publication Date: 2009

Subjects

Subjects / Keywords: Computer Science
Artificial Neural Networks
Artificial Intelligence
Genre: bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: This thesis proposes a novel model of artificial neural networks wherein the notion of synaptic weights is removed and a Gaussian activation function is used. The new, sporadically-connected Neural Networksare trained by a probabilistic extension of the famous error-backpropagationalgorithm and tested using, a set of standard benchmarking rules and problem sets. Despite its simplicity, the proposed model is shown to be capable of generalizing on real-world data with a performance comparable to that of a Gaussian-activated weighted network. We then explore the possible advantages the model might have for efficient FPGA hardware implementations and the biological relevance it has with the current understanding and modeling of neuroplasticity.
Statement of Responsibility: by Chris Caswell
Thesis: Thesis (B.A.) -- New College of Florida, 2009
Electronic Access: RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE
Bibliography: Includes bibliographical references.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Local: Faculty Sponsor: Henckell, Karsten; McDonald, Patrick

Record Information

Source Institution: New College of Florida
Holding Location: New College of Florida
Rights Management: Applicable rights reserved.
Classification: local - S.T. 2009 C35
System ID: NCFE004063:00001

Permanent Link: http://ncf.sobek.ufl.edu/NCFE004063/00001

Material Information

Title: Synaptic Neural Networks--Supervised Learning Without Weights
Physical Description: Book
Language: English
Creator: Caswell, Chris
Publisher: New College of Florida
Place of Publication: Sarasota, Fla.
Creation Date: 2009
Publication Date: 2009

Subjects

Subjects / Keywords: Computer Science
Artificial Neural Networks
Artificial Intelligence
Genre: bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: This thesis proposes a novel model of artificial neural networks wherein the notion of synaptic weights is removed and a Gaussian activation function is used. The new, sporadically-connected Neural Networksare trained by a probabilistic extension of the famous error-backpropagationalgorithm and tested using, a set of standard benchmarking rules and problem sets. Despite its simplicity, the proposed model is shown to be capable of generalizing on real-world data with a performance comparable to that of a Gaussian-activated weighted network. We then explore the possible advantages the model might have for efficient FPGA hardware implementations and the biological relevance it has with the current understanding and modeling of neuroplasticity.
Statement of Responsibility: by Chris Caswell
Thesis: Thesis (B.A.) -- New College of Florida, 2009
Electronic Access: RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE
Bibliography: Includes bibliographical references.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Local: Faculty Sponsor: Henckell, Karsten; McDonald, Patrick

Record Information

Source Institution: New College of Florida
Holding Location: New College of Florida
Rights Management: Applicable rights reserved.
Classification: local - S.T. 2009 C35
System ID: NCFE004063:00001


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