Neuron in a Box: A biologically feasible neural network learning paradigm

Christopher MacLeod

Neuron in a Box: A biologically feasible neural network learning paradigm

Christopher MacLeod

Publikation

4 Zitate (Scopus)

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Abstract

This technical report briefly outlines the operation a biologically feasible artificial neural network training algorithm titled "Neuron in a Box". A description of the algorithm is given together with references to its original publication and use.

Originalsprache	English
Medium	Report
Herausgeber (Verlag)	Lews Castle College
Seitenumfang	3
Publikationsstatus	Published - 2019

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title = "Neuron in a Box: A biologically feasible neural network learning paradigm",

abstract = "This technical report briefly outlines the operation a biologically feasible artificial neural network training algorithm titled {"}Neuron in a Box{"}. A description of the algorithm is given together with references to its original publication and use. ",

keywords = "Artificial Neural Network (ANN), training, Learning, evolutionary algorithm, Artificial intelligence, machine learning",

author = "Christopher MacLeod",

year = "2019",

language = "English",

publisher = "Lews Castle College",

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T2 - A biologically feasible neural network learning paradigm

AU - MacLeod, Christopher

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N2 - This technical report briefly outlines the operation a biologically feasible artificial neural network training algorithm titled "Neuron in a Box". A description of the algorithm is given together with references to its original publication and use.

AB - This technical report briefly outlines the operation a biologically feasible artificial neural network training algorithm titled "Neuron in a Box". A description of the algorithm is given together with references to its original publication and use.

KW - Artificial Neural Network (ANN)

KW - training

KW - Learning

KW - evolutionary algorithm

KW - Artificial intelligence

KW - machine learning

M3 - Other contribution

PB - Lews Castle College

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Neuron in a Box: A biologically feasible neural network learning paradigm

Abstract

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