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Another non-biological aspect of ANNs is the type of learning. In ANNs the standard training method is backpropagation (Rumelhart et al., 1985), where after presenting an input example, each neuron receives its specific error signal which is used to update the weight matrix. It seems unlikely that such a neuron- specific.
Learning internal representations by error propagation – Learning internal representations by error propagation. BibTeX; EndNote;. Delayed learning on internal memory network and organizing internal states,
Selective Presynaptic Propagation of Long-Term Potentiation in. – May 1, 2000. Such selective propagation suggests the existence of a long-range cytoplasmic signaling within the presynaptic neuron, leading to a specific pattern of. (1986 ) Learning internal representations by error propagation. in Parallel distributed processing, eds Feldman JA, Hayes PJ, Rumelhart DE (MIT,
In the case of using a neural network trained with the back propagation algorithm  as a classifier, initial weights of the neural network may cause the problem of finding local. D.E. Rumelhart, G.E. Hinton, and R.J. Williams, "Learning Internal Representations by Error Propagation," Parallel Distributed Processing, vol. 1.
Learning Internal Representations by Error-Propagation. BibTeX @INCOLLECTION. TITLE = "Learning Internal Representations by Error-Propagation",
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A free-energy principle has been proposed recently that accounts for action, perception and learning. by its internal states (for example, neuronal activity and connection strengths). The recognition density is a probabilistic.
Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing.
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This paper presents a generalization of the perception learning procedure for learning the correct sets of connections for arbitrary networks. The rule, falled the.
An abstract is not available. 1986 backpropagation function-approximation learning neural-network. Learning internal representations by error propagation. by:.