Model of homeostatic artificial neuron

Authors:
Martin Ruzek;Tomas Brandejsky
Affiliations:
Department of informatics and telecommunications, Czech Technical University in Prague, Praha 1, Czech Republic;Department of informatics and telecommunications, Czech Technical University in Prague, Praha 1, Czech Republic
Venue:
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Year:
2010

Citing 3
Cited 1

The optimal multi-layer structure of backpropagation networks

NN'06 Proceedings of the 7th WSEAS International Conference on Neural Networks
Generalized models based on neural networks and multiple linear regression

CSECS'06 Proceedings of the 5th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing
A neuro-fuzzy model for function point calibration

WSEAS Transactions on Information Science and Applications

EnergyLife: pervasive energy awareness for households

Proceedings of the 12th ACM international conference adjunct papers on Ubiquitous computing - Adjunct

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a model of homeostatic neuron that is able to find its state of equilibrium by observing the others neurons weights. This method is based on measuring the weights that the other neurons of the neural network assign to the output of the reference neuron, and on improving the parameters of the reference neuron in order to maximize the weights of the other neurons. The basic presumption is that the neuron is trying to maximize its importance in the whole network, which means that it is trying to maximize the values of the weights of the other neurons. The neuron is changing its own input weights and is measuring the reaction of the other neurons. Several types of learning are presented, depending on the way in which the importance of the weights is evaluated.