  In this paper, we present neuro-robotics models with a deep artificial neural
network capable of generating finger counting positions and number estimation.
We first train the model in an unsupervised manner where each layer is treated
as a Restricted Boltzmann Machine or an autoencoder. Such a model is further
trained in a supervised way. This type of pre-training is tested on our
baseline model and two methods of pre-training are compared. The network is
extended to produce finger counting positions. The performance in number
estimation of such an extended model is evaluated. We test the hypothesis if
the subitizing process can be obtained by one single model used also for
estimation of higher numerosities. The results confirm the importance of
unsupervised training in our enumeration task and show some similarities to
human behaviour in the case of subitizing.
