New Ideas for Brain Modelling 4

TL;DR

This paper proposes a cognitive model inspired by the human brain, introducing new methods in image processing and behavior simulation, emphasizing neural binding and dynamic decision-making.

Contribution

It introduces novel image processing and behavior simulation techniques within a brain-inspired cognitive model, integrating neural binding concepts.

Findings

Grid-like image processing with full linking improves pattern association.

A new feedback-based prediction equation enhances behavior simulation.

Binary-analog interfaces can explain neural binding and variable signals.

Abstract

This paper continues the research that considers a new cognitive model based strongly on the human brain. In particular, it considers the neural binding structure of an earlier paper. It also describes some new methods in the areas of image processing and behaviour simulation. The work is all based on earlier research by the author and the new additions are intended to fit in with the overall design. For image processing, a grid-like structure is used with 'full linking'. Each cell in the classifier grid stores a list of all other cells it gets associated with and this is used as the learned image that new input is compared to. For the behaviour metric, a new prediction equation is suggested, as part of a simulation, that uses feedback and history to dynamically determine its course of action. While the new methods are from widely different topics, both can be compared with the binary-analog type of interface that is the main focus of the paper. It is suggested that the simplest of linking between a tree and ensemble can explain neural binding and variable signal strengths.