Formalization of the principles of brain Programming (Brain Principles Programming)

TL;DR

This paper formalizes brain principles programming (BPP) using category theory and integrates models from cognitive science to develop algorithms for simulating brain functions.

Contribution

It introduces a formalization of BPP combining category theory with established cognitive models, enabling algorithm development for brain information processing.

Findings

Formalization of BPP using category theory.

Development of algorithms based on cognitive science models.

Computer experiments demonstrating algorithm operation.

Abstract

In the monograph "Strong artificial intelligence. On the Approaches to Superintelligence" contains an overview of general artificial intelligence (AGI). As an anthropomorphic research area, it includes Brain Principles Programming (BPP) -- the formalization of universal mechanisms (principles) of the brain work with information, which are implemented at all levels of the organization of nervous tissue. This monograph contains a formalization of these principles in terms of category theory. However, this formalization is not enough to develop algorithms for working with information. In this paper, for the description and modeling of BPP, it is proposed to apply mathematical models and algorithms developed earlier, which modeling cognitive functions and base on well-known physiological, psychological and other natural science theories. The paper uses mathematical models and algorithms of the following theories: P.K.Anokhin Theory of Functional Brain Systems, Eleanor Rosch prototypical categorization theory, Bob Rehder theory of causal models and "natural" classification. As a result, a formalization of BPP is obtained and computer experiments demonstrating the operation of algorithms are presented.