Abstract: The present paper describes a new type of neural networks - multidimensional neural-like growing networks. Multidimensional neural-like growing networks are a dynamic structure, which varies depending on the external information received by receptors and the information coming from the effector area to the outside world. Multidimensional receptor-effector neural-like growing networks are supposed to store and process images of objects or situations in the subject area and manage actions through a variety of spatial representations of information, such as tactile, visual, acoustic, taste, etc. Multidimensional receptor-effector neural-like growing networks are used to design intelligent systems and electronic brains of robots. The article describes the neural-like growing networks, the basic rules for constructing the neural-like growing networks and their comparison with the normal neural networks, modeling of information flows in a human body and basic blocks and functions of electronic brains of intelligent systems and robots.

Vitaliy Yashchenko. “Multidimensional Neural-Like Growing Networks - A New Type of Neural Network”. International Journal of Advanced Computer Science and Applications (IJACSA) 6.4 (2015). http://dx.doi.org/10.14569/IJACSA.2015.060401

@article{Yashchenko2015,
title = {Multidimensional Neural-Like Growing Networks - A New Type of Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.060401},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060401},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {4},
author = {Vitaliy Yashchenko}
}