Training of Deep Learning Neuro-Skin Neural Network
This work addresses the challenge of training a new type of neural network for specific contraction tasks, but it appears incremental as it builds on the authors' prior Neuro-Skin concept without broad application or major breakthroughs.
The authors developed a learning algorithm for a novel neural network architecture called Neuro-Skin, which uses finite elements to model cells with neurons, and trained it to contract in response to inputs using sensitivity analysis, showing gradual improvement toward desired responses.
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.