  In this brief paper, a learning algorithm is developed for Deep Learning
Neuro-Skin Neural Network to improve their learning properties. Neuroskin is a
new type of neural network presented recently by the authors. It is comprised
of a cellular membrane which has a neuron attached to each cell. The neuron is
the cells nucleus. A neuroskin is modelled using finite elements. Each element
of the finite element represents a cell. Each cells neuron has dendritic fibers
which connects it to the nodes of the cell. On the other hand, its axon is
connected to the nodes of a number of different neurons. The neuroskin is
trained to contract upon receiving an input. The learning takes place during
updating iterations using sensitivity analysis. It is shown that while the
neuroskin can not present the desirable response, it improves gradually to the
desired level.
