%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{fiesler-92.01,
         author = {Fiesler, Emile},
       keywords = {artificial neural network, connectionism, definition, formalization, mnemonic notation, neural computing, neural network statics, neurocomputing, nomenclature, standardization, terminology, topological taxonomy},
       projects = {Idiap},
          title = {Neural Network Formalization},
           type = {Idiap-RR},
         number = {Idiap-RR-01-1992},
           year = {1992},
    institution = {IDIAP},
       abstract = {In order to assist the field of neural networks in its maturing, a formalization and a solid foundation are essential. Additionally, to permit the introduction of formal proofs, it is essential to have an all encompassing formal mathematical definition of a neural network. Most neural networks, even biological ones, exhibit a layered structure. This publication shows that all neural networks can be represented as layered structures. This layeredness is therefore chosen as the basis for a formal neural network framework. This publication offers a neural network formalization consisting of a topological taxonomy, a uniform nomenclature, and an accompanying consistent mnemonic notation. Supported by this formalization, both a flexible hierarchical and a universal mathematical definition are presented.},
            pdf = {https://publications.idiap.ch/attachments/reports/1992/92-01.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/1992/92-01.ps.gz},
ipdmembership={neuron learning},
}