  Artificial neural networks which are inspired from the learning mechanism of
brain have achieved great successes in many problems, especially those with
deep layers. In this paper, we propose a nucleus neural network (NNN) and
corresponding connecting architecture learning method. In a nucleus, there are
no regular layers, i.e., a neuron may connect to all the neurons in the
nucleus. This type of architecture gets rid of layer limitation and may lead to
more powerful learning capability. It is crucial to determine the connections
between them given numerous neurons. Based on the principle that more relevant
input and output neuron pair deserves higher connecting density, we propose an
efficient architecture learning model for the nucleus. Moreover, we improve the
learning method for connecting weights and biases given the optimized
architecture. We find that this novel architecture is robust to irrelevant
components in test data. So we reconstruct a new dataset based on the MNIST
dataset where the types of digital backgrounds in training and test sets are
different. Experiments demonstrate that the proposed learner achieves
significant improvement over traditional learners on the reconstructed data
set.
