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• W1994984174 endingPage "45" @default.
• W1994984174 startingPage "9" @default.
• W1994984174 abstract "The main contribution of this letter is the derivation of a steepest gradient descent learning rule for a multilayer network of theta neurons, a one-dimensional nonlinear neuron model. Central to our model is the assumption that the intrinsic neuron dynamics are sufficient to achieve consistent time coding, with no need to involve the precise shape of postsynaptic currents; this assumption departs from other related models such as SpikeProp and Tempotron learning. Our results clearly show that it is possible to perform complex computations by applying supervised learning techniques to the spike times and time response properties of nonlinear integrate and fire neurons. Networks trained with our multilayer training rule are shown to have similar generalization abilities for spike latency pattern classification as Tempotron learning. The rule is also able to train networks to perform complex regression tasks that neither SpikeProp or Tempotron learning appears to be capable of." @default.
• W1994984174 created "2016-06-24" @default.
• W1994984174 creator A5003489427 @default.
• W1994984174 creator A5049282767 @default.
• W1994984174 creator A5069060586 @default.
• W1994984174 date "2009-01-01" @default.
• W1994984174 modified "2023-10-16" @default.
• W1994984174 title "Spike-timing error backpropagation in theta neuron networks." @default.
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• W1994984174 doi "https://doi.org/10.1162/neco.2008.09-07-610" @default.
• W1994984174 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/19431278" @default.
• W1994984174 hasPublicationYear "2009" @default.
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