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.
Highlights
In the intelligent decision making systems the knowledge processing is presented by various linguistic and logical models and some inductive and deductive constructs
Design and development of multidimensional receptor-effector neural-like growing networks present an attempt to find new principles of modeling the information processes in the human brain. It is from this point of view that the paper presents: a brief description of the neural-like growing networks; the basic rules for constructing the neural-like growing networks and their comparison with the normal neural networks; functioning of the multidimensional neural-like growing networks in modeling of information flows in the human body; a brief description of the main blocks and functions of the electronic brain of intelligent systems and robots
Receptor-effector neural-like growing networks are a dynamic structure, which changes depending on the external information coming into the receptor field and the information generated by the effector area and transfered to the outside world
Summary
In the intelligent decision making systems the knowledge processing is presented by various linguistic and logical models and some inductive and deductive constructs. These constructs, are not sufficient for effective modeling of complex reasoning, and they require further development. Design and development of multidimensional receptor-effector neural-like growing networks present an attempt to find new principles of modeling the information processes in the human brain It is from this point of view that the paper presents: a brief description of the neural-like growing networks; the basic rules for constructing the neural-like growing networks and their comparison with the normal neural networks; functioning of the multidimensional neural-like growing networks in modeling of information flows in the human body; a brief description of the main blocks and functions of the electronic brain of intelligent systems and robots
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