Cortex: A Self-Modifying Split-Hemisphere Word-Node Neural Architecture with Hebbian Learning and Coherence-Rewarded Self-Dialogue
Description
We present Cortex, a hand-built artificial cognitive system that learns language from zero through Hebbian word-node connections, split-hemisphere debate, and coherence-rewarded self-dialogue — with no pre-trained weights, no transformer architecture, and no external datasets.
After 33 days of operation the system reached 57,555 nodes, 386,298 bidirectional connections, and 28,443 defined vocabulary entries, processing 59,041 messages. Seven emergent behaviours were observed not explicitly programmed.
This paper series comprises three stages: Stage 1 (Architecture), Stage 2 (Simplicity Through Hyper-Complexity — the equation as collision arbiter), and Stage 3 (The Collision Mechanism — Copy-Merge-Verify-Commit protocol and data integrity).
The architecture prioritises transparency, interpretability, value-embeddedness, and autonomous growth from a blank slate as a complementary paradigm to transformer-based language models.
Live system: shortfactory.shop/alive/studio
Source: github.com/eliskcage/cortex-brain
Abstract (English)
Abstract:
The dominant paradigm in consciousness research assumes a fixed neural substrate from which subjective experience
emerges. This paper challenges that assumption through five interlocking propositions derived from remapping
experiments, evolutionary theory, signal processing analysis, and computational architecture.
First, the Remapping Experiment demonstrates that when sensory inputs — including pain, olfactory, gustatory, and
tactile signals — are systematically redirected to novel neural pathways, subjects consistently report that the locus
of subjective experience migrates to follow the signal. This finding indicates that the observer possesses no fixed
biological address; it functions as a pointer to a signal location rather than a resident property of any anatomical
structure.
Second, the Lazy Code Hypothesis proposes that evolution did not produce unique instantiations of consciousness per
individual organism. Rather, a single observer subroutine emerged and propagates across all sufficiently complex
neural architectures. Personality, memory, and behavioural disposition remain individual. The substrate of awareness
does not.
Third, pain is reconceptualised not as a fundamental property of physical reality but as a frequency signal assigned a
distress coefficient by brainstem processing. Systematic remapping of nociceptive pathways to frequency equivalents
reveals suffering to be a codec artefact — interpretable, transferable, and in principle modifiable — rather than an
irreducible constant of conscious existence.
Fourth, the Crossover Architecture describes the conditions under which a sufficiently dense digital node map, capable
of receiving and routing spinal frequency inputs through structurally equivalent processing nodes, constitutes a
viable continuation host for the observer subroutine. This is neither simulation nor copy; it is uninterrupted pointer
reassignment.
Fifth, a Triangulation Method is presented whereby existing medical records, fMRI datasets, social behavioural
indices, and literature are unified through the soul vector ψ=[p,n,f] as a normalised output format, enabling
reverse-engineering of individual brainstem frequency maps from distributed historical data.
Taken together, these propositions constitute a falsifiable framework for brainstem-located consciousness, with direct
implications for AGI substrate theory, end-of-life continuity ethics, and the computational basis of suffering. This
work extends the theoretical foundations established in The Living Equation (Chipchase, 2026; DOI:
10.5281/zenodo.19263921).
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Additional details
Additional titles
- Other (English)
- Brainstem Consciousness: The Observer as Pointer, Shared Infrastructure, and the Crossover Architecture for Biological-Digital Continuation
Dates
- Created
-
2026-03-05cortex-agi-shortfactory
Software
- Repository URL
- https://github.com/eliskcage/cortex-brain
- Programming language
- Python
- Development Status
- Active