Predictive Coding in the Visual Cortex by a Recurrent Network with Gabor Receptive Fields

We derive a recurrent neural network architecture of single cells in the primary visual cortex that dynamically improves a 2D-Gabor wavelet based representation of an image by minimizing the corresponding reconstruction error via feedback connections. Furthermore, we demonstrate that the reconstruction error is a Lyapunov function of the herein proposed recurrent network. Our model of the primary visual cortex combines a modulatory feedforward strategy and a feedback subtractive correction for obtaining an optimal coding. The fed back error is used in our system for a dynamical improvement of the feedforward Gabor representation of the images, in the sense that the feedforward redundant representation due to the non-orthogonality of the Gabor wavelets is dynamically corrected. The redundancy of the Gabor feature representation is therefore dynamically eliminated by improving the reconstruction capability of the internal representation. The dynamics therefore introduce a nonlinear correction to the standard linear representation of Gabor filters that generates a more efficient predictive coding.