In this paper we propose a modification of the Cognitive Architectures for Sensory Processing proposed by Chalasani and Principe. Here we keep the bottom-up data representation through generative models as before, but propose a top-down flow based on backpropagation of gradients for recognition. By treating the bottom-up procedure involved in the inference step as a recursive neural network, we show that supervised learning can be used in conjunction with other layers commonly used for Deep Learning. Also, this allows us to learn models that incorporate at the same time data classification and statistical modeling of the input. We show that this combination provides classification results that are robust to input noise.