Conceptual and Design Principles for a Self-Referential Algorithm Mimicking Neuronal Assembly Functions

TL;DR

This paper introduces the Environment Generative Operator (EGO), a self-referential algorithmic framework that models cognitive processes and neuronal assembly functions based on experience and vital equilibrium preservation.

Contribution

It presents a novel formalization of cognitive modeling using a self-referential language and an implemented prototype demonstrating the approach.

Findings

EGO prototype (EGO-P) has been implemented and tested.

The method successfully simulates cognitive processes as operations on neuron assemblies.

The approach aligns with Hebbian understanding of neuronal functions.

Abstract

This article proposes a method to formalise models of cognitive processes grounded in experience, considering experience from the perspective of a living system and not from that of an observer of the living system. The perspective of a living system is defined by the need of the system to preserve the vital equilibria. The method is based on an algorithmic schema that we call Environment Generative Operator (EGO) and uses a self-referential language developed for this purpose which we call E-language. EGO simulates cognitive processes as operations on neuron assemblies as understood by Hebb. In this article we present an EGO prototype (EGO-P) which has already been implemented and tested.