GeneRec
GeneRec is a generalization of the Recirculation algorithm, and approximates Almeida-Pineda recurrent backpropagation.[1][2] It is used as part of the Leabra algorithm for error-driven learning.[3]

GeneRec is a generalization of the Recirculation algorithm, and approximates Almeida-Pineda recurrent backpropagation.[1][2] It is used as part of the Leabra algorithm for error-driven learning.[3]

The symmetric, midpoint version of GeneRec is equivalent to the contrastive Hebbian learning algorithm (CHL).[1]

See also[edit]

References[edit]

  1. ^ Jump up to:a b O'Reilly, R.C. Biologically Plausible Error-driven Learning using Local Activation Differences: The Generalized Recirculation Algorithm. Neural Computation, 8, 895-938. Abstract PDF
  2. Jump up^ GeneRec description in Computational explorations in cognitive neuroscience: understanding the mind by Randall C. O'Reilly,Yuko Munakata
  3. Jump up^ Leabra overview in Emergent
Immediately related elementsHow this works
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Machine Learning Methods & Algorithms Â»Machine Learning Methods & Algorithms
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Supervised learning Â»Supervised learning
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Artificial neural network Â»Artificial neural network
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Backpropagation Â»Backpropagation
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Almeida–Pineda recurrent backpropagation Â»Almeida–Pineda recurrent backpropagation
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GeneRec
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Error-driven learning Â»Error-driven learning
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