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PubMed · 14268953

LIMITATIONS ON COMPLEXITY OF RANDOM LEARNING NETWORKS.

F OFFNER
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Abstract

Randomly connected networks can be made adaptive, and thus able to "learn." Signal-to-noise considerations are shown to limit the maximum initial complexity which can learn. A higher order of complexity may be possible in multilayered structures which learn layer-by-layer; or if learning is possible during construction. Perception-like devices would appear not to be operative if of a high order of complexity.

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ComputersComputers, AnalogCyberneticsHumansLearningMathematicsNeurophysiologySensory Receptor CellsCOMPUTERS, ANALOGCYBERNETICSLEARNINGMATHEMATICSNEUROPHYSIOLOGYRECEPTORS, NEURALJournal Article

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F OFFNER. 1965. LIMITATIONS ON COMPLEXITY OF RANDOM LEARNING NETWORKS.. https://doi.org/10.1016/s0006-3495(65)86710-8

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