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About this paper
Leon Lufkin, Ashish Puri, Ganlin Song, Xinyi Zhong, John Lafferty
The authors present a continuous-activation model where local excitation and inhibition shape sparse responses as feedforward receptive fields adapt to stimuli. Under a diagonal-dominance condition, activation updates converge to a minimizer. Natural-image training produced pinwheel-like edge-detector maps; in a word-embedding distance comparison, sparse-code fit had R² 0.80 versus 0.39 for t-SNE. These computational results do not establish biological causation.
Summary by alphaXiv.
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