TAXONOMY

Nature Subjects Taxonomy

CONCEPT NAME

Network models

DESCRIPTION

Network models are a computer architecture, implementable in either hardware or software, meant to simulate biological populations of interconnected neurons. These models, also known as perceptrons or multilayer connectionist models, process information based on the pattern and strength of the connections among the neuron-like units that compose them.

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23 TRIPLES      13 PREDICATES      23 URIs      14 LITERALS

Subject Predicate Object
1 subjects:network-models dcterms:modified 2017-10-09T07:40:09+00:00
2 sg:license http://scigraph.springernature.com/explorer/license/
3 rdf:type npg:Subject
4 sg:Subject
5 skos:Concept
6 rdfs:label Network models
7 skos:altLabel Connectionist Model
8 Connectionist Models
9 Neural Network (Computer)
10 Neural Network Model
11 Neural Network Models
12 Neural Networks (Computer)
13 Perceptron
14 Perceptrons
15 skos:broader subjects:computational-neuroscience
16 skos:closeMatch dbpedia:Artificial_neural_network
17 mesh:D016571
18 skos:definition Network models are a computer architecture, implementable in either hardware or software, meant to simulate biological populations of interconnected neurons. These models, also known as perceptrons or multilayer connectionist models, process information based on the pattern and strength of the connections among the neuron-like units that compose them.
19 skos:hiddenLabel network-models
20 skos:historyNote [skos:definition - 2014.01.21] A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.
21 skos:inScheme subjects:
22 skos:notation network-models
23 skos:prefLabel Network models
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