A Hybrid Training Algorithm for Continuum Deep Learning Neuro-Skin Neural Network

TL;DR

This paper introduces a hybrid training algorithm for the novel Deep Learning NeuroSkin Neural Network, which models neural tissue with finite elements and improves its response through iterative learning with sensitivity analysis.

Contribution

The paper presents a new training algorithm specifically designed for the NeuroSkin neural network, enhancing its learning capabilities and response accuracy.

Findings

Neuroskin can gradually improve its response to desired levels.

The training algorithm utilizes sensitivity analysis during updates.

Neuroskin modeling involves finite element representation of cellular structures.

Abstract

In this brief paper, a learning algorithm is developed for Deep Learning NeuroSkin 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 cannot present the desirable response, it improves gradually to the desired level.