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Nathan M. Thornhill

Researcher in complexity science, information theory, and neuroscience, based in Fort Wayne, Indiana. Came to the work from a clinical healthcare career (nursing assistant through ICU admissions), then taught himself software development; holds no computer science degree. Operates without university affiliation or external funding. Founder of the Institute for Complexity Science and Advanced Computing (ICSAC), a Fort Wayne scholarly publisher that charges authors nothing to publish and serves as

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# Nathan M. Thornhill

> Researcher in complexity science, information theory, and neuroscience, based in Fort Wayne, Indiana. Came to the work from a clinical healthcare career (nursing assistant through ICU admissions), then taught himself software development; holds no computer science degree. Operates without university affiliation or external funding. Founder of the Institute for Complexity Science and Advanced Computing (ICSAC), a Fort Wayne scholarly publisher that charges authors nothing to publish and serves as the canonical publication venue for the work. Measured how much pattern information is lost crossing a dimensional boundary, and derived the mechanism behind that loss. Author of The Internal Dynamics of Decisions, The Existence Threshold, The Dynamic Existence Threshold, The Dimensional Loss Theorem, Architecture-Independent Geometric Memory Failure, and the Recursive Existence Threshold. Holder of four US provisional patents covering geometric encoding, neural network dimensionality, data preservation, and consciousness classification.

Based in Fort Wayne, Indiana, USA. Founder and affiliated researcher at the Institute for Complexity Science and Advanced Computing (ICSAC), identifier ICSAC-00001. Student Member, Association for the Scientific Study of Consciousness (ASSC), since 2026. ORCID: 0009-0009-3161-528X. Contact: research@nathanthornhill.com.

The research program develops a substrate-independent framework for measuring the conditions under which organized patterns persist or dissolve. Core finding: an Integration-Differentiation (I-D) balance metric separates brain 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 and space weather.

## Publications

Most papers are published by ICSAC Open Record (canonical) with DOI archives on Zenodo (CERN); the 2026 dynamics paper is a preprint on Preprints.org.

- [The Internal Dynamics of Decisions: From Rats and Primates to AI (2026)](https://www.preprints.org/manuscript/202608.1095/v2): Preprint on Preprints.org (MDPI), CC BY 4.0; version 2 posted 8 October 2026, version 1 on 17 August 2026. Asks whether the dynamics that carry a decision to commitment differ between the recurrent circuits of the brain and the transformer networks of large language models. A dynamics demix separates input-driven change from change generated by a population's own dynamics; validated on synthetic systems and checked against the mean-field variance theory of driven echo-state networks, it was applied to cortical populations in perceptual and value-based decisions and to five feedforward transformer language models constructing multi-step answers. Every neural dataset was autonomous-dominated and the five language models were input-dominated; for the two models and the cortical dataset re-examined at a matched input dimension, the direction of that difference held, and it is reported
…

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