Training of Deep Learning Neuro-Skin Neural Network

TL;DR

This paper introduces a novel learning algorithm for Deep Learning Neuro-Skin Neural Networks, modeled with finite elements, demonstrating gradual improvement in response accuracy through iterative training and sensitivity analysis.

Contribution

It presents a new neural network model called Neuroskin and develops a training algorithm that enhances its learning capabilities using finite element modeling and sensitivity analysis.

Findings

Neuroskin can be trained to contract in response to inputs.

The learning process gradually improves the network's response accuracy.

Sensitivity analysis aids in the iterative training of the network.

Abstract

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.