In this work, we model the human visual information processing system by approximating the lateral geniculate nucleus (LGN) function using a shallow convolutional network based on a pruned autoencoder (pAE) architecture. Our model integrates feedforward and feedback streams and processes both temporal and non-temporal visual data. Experimental results show that the proposed deep-tuned model achieves 99.26% prediction accuracy.