Open AccessStructure and Functions of a Replicative Neuro-like Module
Author(s) -
Ivan V. Stepanyan,
А.А. Khomich
Publication year - 2020
Publication title -
trudy spiiran
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.242
H-Index - 9
eISSN - 2078-9599
pISSN - 2078-9181
DOI - 10.15622/sp.2020.19.4.4
Subject(s) - computer science , simple (philosophy) , artificial neural network , scalability , artificial intelligence , task (project management) , reservoir computing , basis (linear algebra) , binary number , evolutionary algorithm , theoretical computer science , machine learning , algorithm , recurrent neural network , mathematics , arithmetic , geometry , philosophy , management , epistemology , database , economics
The given work describes a technology of construction of neural network system of artificial intellect (AI) at a junction of declarative programming and machine training on the basis of modelling of cortical columns. Evolutionary mechanisms, using available material and relatively simple phenomena, have created complex intelligent systems. From this, the authors conclude that AI should also be based on simple but scalable and biofeasible algorithms, in which the stochastic dynamics of cortical neural modules allow to find solutions to of complex problems quickly and efficiently.. Purpose: Algorithmic formalization at the level of replicative neural network complexes - neocortex columns of the brain. Methods: The basic AI module is presented as a specialization and formalization of the concept "Chinese room" introduced by John Earle. The results of experiments on forecasting binary sequences are presented. The computer simulation experiments have shown high efficiency in implementing the proposed algorithms. At the same time, instead of using for each task a carefully selected and adapted separate method with partially equivalent restatement of tasks, the standard unified approach and unified algorithm parameters were used. It is concluded that the results of the experiments show the possibility of effective applied solutions based on the proposed technology. Practical value: the presented technology allows creating self-learning and planning systems.