Self-organization of connection patterns within brain areas of animals begins prenatally, and has been shown to depend on internally generated patterns of neural activity. Such activity is genetically controlled and has been proposed to give the neural system an appropriate bias so that it can learn reliably from complex environmental stimuli. We demonstrate this idea computationally using competitive learning networks for recognizing handwritten digits. Animations of the learning process show how training the network with patterns from an evolved pattern generator before training with the actual training set improves learning performance.

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