New Ideas for Brain Modelling 2

TL;DR

This paper presents a simple, self-organizing brain model using nested structures and inhibitory mechanisms to simulate pattern formation, control chaos, and support memory and counting functions.

Contribution

It introduces a novel, mostly mechanical approach to self-organization in brain models, incorporating nested patterns and simple equations for dynamic control.

Findings

Nested pattern structures can serve as counting mechanisms.

Inhibitory equations enable control over brain state transitions.

Self-organization promotes memory maintenance and concept integrity.

Abstract

This paper describes a relatively simple way of allowing a brain model to self-organise its concept patterns through nested structures. For a simulation, time reduction is helpful and it would be able to show how patterns may form and then fire in sequence, as part of a search or thought process. It uses a very simple equation to show how the inhibitors in particular, can switch off certain areas, to allow other areas to become the prominent ones and thereby define the current brain state. This allows for a small amount of control over what appears to be a chaotic structure inside of the brain. It is attractive because it is still mostly mechanical and therefore can be added as an automatic process, or the modelling of that. The paper also describes how the nested pattern structure can be used as a basic counting mechanism. Another mathematical conclusion provides a basis for maintaining memory or concept patterns. The self-organisation can space itself through automatic processes. This might allow new neurons to be added in a more even manner and could help to maintain the concept integrity. The process might also help with finding memory structures afterwards. This extended version integrates further with the existing cognitive model and provides some new conclusions.