document: Preintegration lateral inhibition enhances unsupervised learning A large and influential class of neural network architectures uses postintegration lateral inhibition as a mechanism for competition . We argue that these algorithms are computationally deficient in that they fail to generate , or learn , appropriate perceptual representations under certain circumstances . An alternative neural network architecture is presented here in which nodes compete for the right to receive inputs rather than for the right to generate outputs . This form of competition , implemented through preintegration lateral inhibition , does provide appropriate coding properties and can be used to learn such representations efficiently . Furthermore , this architecture is consistent with both neuroanatomical and neuropsychological data . We thus argue that preintegration lateral inhibition has computational advantages over conventional neural network architectures while remaining equally biologically plausible
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