Consciousness researcher and small business owner in Fort Wayne, Indiana. Founder of ICSAC, owner of 3Rivers WebTech, creator of CiteStamp. Stereotypical dad.
Support independent research at ICSAC →
"To exist is to continually overcome loss"
I'm an independent researcher in Fort Wayne, Indiana — complexity science, information theory, computational physics. No university, no grant, no committee deciding what I work on. Nobody handed me any of this.
I came to it from healthcare: nursing assistant up through nursing-home administration and ICU admissions. Years on the floor, watching people cross the line between conscious and unconscious, watching what holds together and what comes apart. I was asking what keeps a pattern alive long before I had the math for it. That's still the engine.
My latest paper, the Recursive Existence Threshold, opens with something that should bother people more than it does. A photograph, a screen of static, and the word “TRUE” can score identical on the single number people keep floating as a measure of consciousness. One's a face, one's noise, one's a fact — and the number can't tell them apart, because it was never built to. So I ran a pre-registered test across three substrates — a large language model, the anaesthetized brain, and sleep — to find where the discarded information lives. Every time: in the structure, not the scalar. Whatever consciousness is, it doesn't fit inside one number.
That grew out of the Dynamic Existence Threshold, which put a number on the boundary I'd watched on the ICU floor — an integration–differentiation balance that separates a conscious brain from an unconscious one at AUC 0.91 across 136,394 EEG recordings. Under it sit three foundational papers: the Existence Threshold defined what pattern persistence requires, the 86% Scaling Law measured how much pattern information is lost crossing a dimensional boundary, and the Dimensional Loss Theorem derived the mechanism behind that loss.
The AI implication: the same metric runs on neural networks and large language models — a substrate-independent test for whether a system has real organizational coherence or just a convincing impression of it. I hold a US provisional patent on it.
I publish through the Institute for Complexity Science and Advanced Computing because I founded it. No PhD, no advisor, no journal willing to take an outsider's stack of papers seriously — so I built the venue. It charges authors nothing to publish. Science shouldn't be gatekept and paywalled, and you shouldn't need an institution behind you to clear the gate. Every paper also gets a permanent DOI on Zenodo (CERN), and the work's been picked up by complexity communities at UABC in México and the Kapodistrian Academy in Greece. Outsider doesn't mean wrong.
When I'm not doing this, I run 3Rivers WebTech out of Fort Wayne, building websites for small businesses across northeast Indiana. I taught myself to code; no computer science degree, no bootcamp. It keeps the lights on. I also built CiteStamp, which catches the citation your AI hallucinated. It's free, because the researchers who need it most are the ones without a university library behind them.
Off the clock: family, the lawn, the house. The to-do list always wins.
Simple parts, simple rules, behavior nobody designed. One bird just keeps its distance from its neighbors; ten thousand become a murmuration. Neurons do it and you get a mind. Traders do it and you get a crash. Water molecules do it and you get weather that spans a continent.
The thread running through all of it is emergence — the whole becomes more than the sum of its parts. A few ideas carry the field: self-organization (order with nobody in charge), phase transitions (the tipping point where a system flips states), and feedback loops (outputs that circle back and reshape what comes next). These aren't metaphors. They're measurable patterns that repeat across biology, economics, physics, and computing — the same math that describes ice melting describes a healthy brain sliding into a seizure or a stable economy tipping into recession. My work builds tools to catch those transitions before they happen.
In 1948 Claude Shannon published A Mathematical Theory of Communication and handed us the math of information — how to measure it, move it, and store it. One hard floor sits under all of it: information comes in bits, and every channel has a limit on what it can carry without loss. That one idea runs your phone calls, your compression, your whole network stack.
The key quantity is entropy — how much surprise a message carries. High entropy: unpredictable, information-dense. Low entropy: redundant, predictable. It long ago outgrew telecom — biologists read DNA with it, physicists aim it at black holes, neuroscientists use it to gauge how complex brain activity is.
I use it for a narrower question: how does a pattern survive crossing a boundary, and how does a system hold its organization together? The 86% Scaling Law measures exactly how much information makes it across a dimensional boundary. The Dynamic Existence Threshold uses the same tools to catch a system losing its structure — a brain, a market, or the sun's magnetic field.
All publications are archived across multiple platforms to ensure permanent accessibility and proper attribution:
The collected volume. Four open-access papers — The Existence Threshold, the 86% Scaling Law, the Dimensional Loss Theorem, and the Dynamic Existence Threshold — joined by three original bridge essays and front matter that trace the framework from binary discrete systems through dimensional embedding to dynamic Phi. 100 pages, 7×10 trade paperback. ISBN 979-8-9958925-0-2 · LCCN 2026941912.
The substrate-neutral scalar that keeps getting proposed as a sufficiency index for consciousness is provably content-blind by construction: a photograph, white noise, and the word “TRUE” can be matched on density, lose the same organization scalar, and keep an identical 4/13 of their connectivity. So where does the information live that the scalar throws away? One pre-registered test, three substrates, three answers. In a transformer, factual truth is linearly decodable from the residual stream (AUC 0.83) while the scalar stays blind across all 29 layers. In the anaesthetized brain, which individual a recording belongs to decodes from leakage-controlled connectivity where the scalar sits at chance. In sleep, a recurrence measure survives residualizing the scalar out across five stage contrasts. A sufficiency predicate for consciousness cannot be a single global scalar — what it leaves out is multiply-realizable relational structure that a transformer’s residual stream and the brain’s thalamocortical loops both carry.
Two Parallel Lines of Evidence. A synthesis note recording the chronology of two independent lines of evidence that converge on architecture-independent geometric fixed points as the principal explanatory mechanism for representational memory failure: the 86% Scaling Law (Thornhill 2026b) and the Dimensional Loss Theorem with GPT-2/Gemma-2 validation (Thornhill 2026c) from January 2026, and the Sentra production-embedding study (Barman, Starenky, Bodnar, Narasimhan, Gopinath, March 2026) reporting variance concentration to ~16 effective dimensions via participation-ratio methodology. The two bodies of work use different metrics and report different specific quantities, but converge on the same architecture-independent geometric explanation.
Integration-Differentiation Balance Predicts System State Across Substrates. A cross-domain framework for detecting organizational dissolution. Demonstrates that a structural coupling metric (Integration-Differentiation balance) separates consciousness states at AUC 0.91 across 136,394 EEG recordings, convergent with a simpler spectral baseline on the same task and has been applied to financial markets, space weather, and neural data.
A physicalist framework for understanding consciousness through information thermodynamics. Establishes the theoretical conditions that define the boundary between existence and non-existence, exploring how systems maintain coherence against entropy.
The first quantitative measurement of information loss at dimensional boundaries. Measures a consistent 86.01% ± 2.39% information loss across the tested cellular automata systems, with weak scale-dependence across grid sizes.
A formal mathematical theorem describing the mechanisms underlying information loss during dimensional reduction. Provides the theoretical foundation for understanding how patterns persist—or fail to persist—across dimensional boundaries.
Selected for distribution through the Social Science Research Network ejournal system
Selected for distribution in Information Theory & Research, Vol. 7, No. 71
Selected for distribution in Generative AI, Vol. 4, Issue 115
Selected for distribution in Information Theory & Research, Vol. 7, Issue 64
Selected for distribution in Computer Science Education, Vol. 9, Issue 107
Selected for distribution in Information Theory & Research, Vol. 7, No. 51
Selected for distribution in Generative AI, Vol. 4, No. 73
Selected for distribution in Information Systems, Vol. 9, No. 69
Selected for distribution in Computer Science Education, Vol. 9, No. 55
Selected for distribution in Computer Science Education, Vol. 9, No. 54
Selected for distribution in Information Theory & Research, Vol. 7, No. 29
Selected for distribution in Artificial Intelligence, Vol. 9, No. 47
Selected for distribution in Advanced Theoretical Physics and Mathematics Community — Kapodistrian Academy of Science (Greece)
The research as talks, in plain language. Both run on live demonstrations, not slides.
Brain, or AI model? The Internal Dynamics of Decisions: From rats and primates to AI
One analysis, run live on rat cortex, primate cortex, and language models: do they commit to a decision the same way?
Trust me, it's cited: catching AI's invented sources live
Your AI writes a confident bibliography. Some of it doesn't exist. The audience tries to spot the fakes; then we check them live.
Abstracts and booking are on Sessionize. Or email me.
Methods and Systems for Consciousness Classification and Complex System Monitoring
Systems and Methods for Adversarial Geometric Encoding to Preserve Information Across Dimensional Boundaries
Systems and Methods for Optimal Dimensional Encoding in Neural Networks
Complete Three-Dimensional Geometric Encoding System for Data Preservation and Analysis
Their words, not mine.
Fort Wayne Small Business Owner’s AI, Neuroscience Research Heads to UC San Diego Conference