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Leabra stands for local, error-driven and associative, biologically realistic algorithm. It is a model of learning which is a balance between Hebbian and error-driven learning with other network-derived characteristics. This model is used to mathematically predict outcomes based on inputs and previous learning influences. This model is heavily influenced by and contributes to neural network designs and models. This algorithm is the default algorithm in emergent (successor of PDP++) when making a new project, and is extensively used in various simulations.

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rdf:type	yago:WikicatArtificialNeuralNetworks yago:Abstraction100002137 yago:Communication100033020 yago:ComputerArchitecture106725249 yago:Description106724763 yago:Message106598915 yago:NeuralNetwork106725467 yago:Specification106725067 yago:Statement106722453
rdfs:label	Leabra (en)
rdfs:comment	Leabra stands for local, error-driven and associative, biologically realistic algorithm. It is a model of learning which is a balance between Hebbian and error-driven learning with other network-derived characteristics. This model is used to mathematically predict outcomes based on inputs and previous learning influences. This model is heavily influenced by and contributes to neural network designs and models. This algorithm is the default algorithm in emergent (successor of PDP++) when making a new project, and is extensively used in various simulations. (en)
dcterms:subject	Machine learning algorithms Artificial neural networks
Wikipage page ID	7049330 (xsd:integer)
Wikipage revision ID	1102519174 (xsd:integer)
Link from a Wikipage to another Wikipage	Neural network Almeida–Pineda recurrent backpropagation Dopaminergic Error-driven learning GeneRec Emergent (software) Go (programming language) Contrastive Hebbian learning Computer simulation Temporal difference learning Machine learning algorithms Winner-take-all (computing) Basal ganglia Prefrontal cortex Artificial neural networks Learning Spiking neural network Classical conditioning Midbrain Neurons Working memory PVLV PBWM Hebbian learning Conditional principal components analysis dbr:Recirculation_algorithm
Link from a Wikipage to an external page	ftp://grey.colorado.edu/pub/oreilly/thesis/oreilly_thesis.all.pdf http://archive.cnbc.cmu.edu/Resources/PDP++/manual/pdp-user_235.html https://cran.r-project.org/web/packages/leabRa/ https://cran.r-project.org/web/packages/leabRa/vignettes/leabRa.html http://grey.colorado.edu/emergent/index.php/Leabra https://grey.colorado.edu/emergent/index.php/Main_Page https://grey.colorado.edu/svn/emergent/emergent/trunk/Matlab/ https://psychology.ucdavis.edu/people/oreilly
sameAs	Leabra Leabra Leabra Leabra
dbp:wikiPageUsesTemplate	dbt:Reflist
has abstract	Leabra stands for local, error-driven and associative, biologically realistic algorithm. It is a model of learning which is a balance between Hebbian and error-driven learning with other network-derived characteristics. This model is used to mathematically predict outcomes based on inputs and previous learning influences. This model is heavily influenced by and contributes to neural network designs and models. This algorithm is the default algorithm in emergent (successor of PDP++) when making a new project, and is extensively used in various simulations. Hebbian learning is performed using conditional principal components analysis (CPCA) algorithm with correction factor for sparse expected activity levels. Error-driven learning is performed using GeneRec, which is a generalization of the , and approximates Almeida–Pineda recurrent backpropagation. The symmetric, midpoint version of GeneRec is used, which is equivalent to the contrastive Hebbian learning algorithm (CHL). See O'Reilly (1996; Neural Computation) for more details. The activation function is a point-neuron approximation with both discrete spiking and continuous rate-code output. Layer or unit-group level inhibition can be computed directly using a k-winners-take-all (KWTA) function, producing sparse distributed representations. The net input is computed as an average, not a sum, over connections, based on normalized, sigmoidally transformed weight values, which are subject to scaling on a connection-group level to alter relative contributions. Automatic scaling is performed to compensate for differences in expected activity level in the different projections. Documentation about this algorithm can be found in the book "Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain" published by MIT press. and in the Emergent Documentation (en)
prov:wasDerivedFrom	wikipedia-en:Leabra?oldid=1102519174&ns=0
page length (characters) of wiki page	7003 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Leabra
is Link from a Wikipage to another Wikipage of	Prefrontal cortex basal ganglia working memory GeneRec O'Reilly Emergent (software) Hebbian theory Randall C. O'Reilly Outline of machine learning PVLV
is known for of	Randall C. O'Reilly
is known for of	Randall C. O'Reilly
is foaf:primaryTopic of	wikipedia-en:Leabra

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