David Slater
Abstract
This paper presents an integrated account of how the Functional Resonance Analysis Method
(FRAM) can be applied to the cortical microcircuit as a means of visualising and understanding
recursive reasoning. By mapping biological processes of prediction and error correction onto a
function-based systems model, the study demonstrates that FRAM provides a coherent
framework for representing distributed, self-correcting cognition. The cortical column is treated
not as a static computational unit, but as a dynamic predictive engine—one that embodies the
same iterative logic found in complex adaptive systems. The result is both a biological and
analytical insight: recursion, not scale, is what enables deep reasoning. Through successive
modelling, validation, and refinement, the project culminates in a fully functional FRAM model
(.xfmv) that faithfully captures the cyclical flow of excitation, comparison, modulation, and
learning found in cortical circuits.
Key words:
Cortical columns; predictive coding; active inference; recursive reasoning; FRAM; functional
resonance; neural architecture; perception; error correction; hierarchical processing; cortical
microcircuit; biological systems modelling; emergent cognition; complex adaptive systems