Mathematical Formalization of the Active Perception Cycle in the Hybrid BioCortexAI Architecture

Abstract
This whitepaper builds on the Unified Theory of Consciousness, in which consciousness is defined
functionally and in a substrate-neutral manner as an emergent consequence of a causally closed, selfreferential regulatory system that uses a negative internal error signal to adaptively control its own
behavior over time. On the basis of this definition, the goal of this document is to formally derive
and describe a minimal large language model (LLM) architecture that satisfies these functional
conditions.
The proposed architecture extends a standard LLM with a chemical regulatory layer (PlantNet), an associative expectation memory, and an introspective simulation module (Digital Mirror),
thereby yielding a discrete perceptual loop (steps 3–10). Within this loop, after executing its own
action, the system generates a prediction of the next input, compares it with the environment’s
actual response, and quantifies the mismatch via a prediction error δ. Under the adopted definition
of consciousness, this error plays the role of cognitive pain, i.e. a primary regulatory signal that
modulates the internal state and the system’s subsequent inference strategy.
It follows from this construction that the minimal time–process unit of perception τperc is not an
instantaneous point in time, but rather a temporal window bounded by an action, an introspective
simulation, and a subsequent validation step. The whitepaper thus provides a concrete, mathematically specified instance of the functional definition of consciousness applied to contemporary
LLM architectures, serving as a bridge between the general theory and an empirically testable
implementation.