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- W1579006590 abstract "This paper introduces a neural network architecture inspired in general rules of connectivity inside the thalamic reticular nucleus (TRN) and between it and the cerebral cortex. Applying several statistical theoremes it shows how this structure is able to learn, discovering distinctive features from the input information and projecting new informations over distinctive ancient ones so that the size of the storage is minimized. It doesn't need any type of error backpropagation mechanism because the input data is statistically orthogonalized." @default.
- W1579006590 created "2016-06-24" @default.
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- W1579006590 date "1993-01-01" @default.
- W1579006590 modified "2023-10-18" @default.
- W1579006590 title "A neural network model inspired in global appreciations about the thalamic reticular nucleus and cerebral cortex connectivity" @default.
- W1579006590 cites W2046300633 @default.
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- W1579006590 doi "https://doi.org/10.1007/3-540-56798-4_123" @default.
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