Hyvarinen A, Hoyer P, 1999, "Emergence of complex cell properties in a neural network that maximises the sparseness of local energies" Perception 28 ECVP Abstract Supplement
Emergence of complex cell properties in a neural network that maximises the sparseness of local energies
A Hyvarinen, P Hoyer
A useful approach to modelling the properties of visual neurons is based on statistical generative models of natural images. Olshausen and Field [1996 Nature (London) 381 607 - 609)] showed that the estimation of a simple linear generative model can be performed by maximising the sparseness of the underlying image components. Sparseness is a statistical property that expresses the 'spiky' non-Gaussian shape of the probability density function of the component. Olshausen and Field used natural image patches as training data (input) to a neural network that learned according to this estimation principle, and observed emergence of linear features that closely resemble simple-cell receptive fields. We show here that this same principle can explain the emergence of the principal properties of complex cells as well. We modelled complex-cell responses using classical (local) energy models, and derived a learning rule for a neural network to maximise the sparseness of the responses. We trained the neural network using 16 × 16 pixel monochrome image patches from natural scenes as the input data. Thus we obtained features that had the principal properties of complex cells: phase and (limited) shift invariance, in addition to orientation and frequency selectivity.
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ECVP 1999 Abstract Supplement (complete) size: 1478 Kb