Lending direction to neural networks
Neural Networks
Probabilistic interpretation of population codes
Neural Computation
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We present a probabilistic population coding model of Gabor filter responses. Based on the analytically derived orientation tuning function and a von Mises mixture model of the filter responses, a probability density function of the local orientation in a given point can be extracted through a parameter estimation procedure. The probability density captures angular information at edges, corners or T-junctions and also yields a contrast invariant description of the certainty of each orientation estimate, which can be characterized in terms of the entropy of the corresponding mixture component.