Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation
Fig 7
Nonlinear Hebbian learning across sensory modalities.
(a) The auditory input is modeled as segments over time and frequency (red) of the spectrotemporal representation of speech signals. (b) The V2 input is assembled from the output of modeled V1 complex cells at different positions and orientations. Receptive fields are represented by bars with size proportional to the connection strength to the complex cell with the respective position and orientation. (c) Strabismic rearing is modeled as binocular stimuli with non-overlapping left and right eye input patches (red). (d-f) Statistical distribution (log scale) of the input projected onto three different features for speech (d), V2 (e) and strabismus (f). In all three cases, the learned receptive field (blue, inset) is characterized by a longer tailed distribution (arrows) than the random (red) and comparative (green) features. (g-i) Relative optimization value for five nonlinearities (same as in Fig 2), for the three selected patterns (insets). The receptive fields learned with the quadratic rectifier nonlinearity (θ1 = 1., θ2 = 2.) are the maxima among the three patterns, for all five nonlinearities, for all three datasets.