  Recent models for image processing are using the Convolutional neural network
(CNN) which requires a pixel per pixel analysis of the input image. This method
works well. However, it is time-consuming if we have large images. To increase
the performance, by improving the training time or the accuracy, we need a
size-independent method. As a solution, we can add a Neuronal Attention model
(NAM). The power of this new approach is that it can efficiently choose several
small regions from the initial image to focus on. The purpose of this paper is
to explain and also test each of the NAM's parameters.
