Reality from Noise
Reality from Noise
Note on Collaborative Authorship This manuscript was co-developed through iterative correspondence between human researcher Robin Langell and several large language models. The primary drafting and theoretical scaffolding were conducted by GPT-5 and GPT-5.1 (OpenAI), contributing to the mathematical formalization and system integration. Critical technical review and iterative "peer review" were performed by Gemini 3 (Google) and GPT-5-Pro (OpenAI), whose rigorous critiques of the sub-coordinate definitions and statistical methodology significantly shaped the final manuscript. All models were used as structured reasoning and writing assistants under the direction and final authorship of Robin Langell.
Reality from Noise unveils a new geometric framework for understanding the mind, proposing that our experience is rhythmically reconstructed from neural noise hundreds of times per minute. By mapping these dynamics, we reveal distinct topological fingerprints for sleep, focus, and mental disorders, moving psychiatry from symptom checklists to precision geometry. It offers a tangible atlas of how the brain navigates reality, bridging the gap between biological rhythms and the subjective landscape of being.
Changelog:v.0.1 Initial release v.0.2 Removed duplicate sections
Consciousness, Neurology/statistics & numerical data, Functional Neuroimaging/methods, Parkinson Disease/pathology, Neuroimaging, Biomarkers/analysis, Neurology/methods, Biomarkers, Pharmacological, topological data analysis, Diffusion, Alzheimer Disease, phenotype–brain mapping, Diffusion Tensor Imaging/psychology, manifold learning, Neuroimaging/methods, Parkinson Disease/psychology, Neurology/classification, Brain Mapping, Stochastic Processes, Clinical Relevance, Consciousness/classification, Epilepsy, Functional Neuroimaging, Noetic Diffusion Theory, Signal Processing, Computer-Assisted, Parkinson Disease, Neurogeometry, Models, Theoretical, Neurology/statistics & numerical data, Nonlinear Dynamics, Neurology, Diffusion Models, Hallucinogens, Alzheimer, Dementia, dynamic functional connectivity, Sleep, geometric signatures of brain states
Consciousness, Neurology/statistics & numerical data, Functional Neuroimaging/methods, Parkinson Disease/pathology, Neuroimaging, Biomarkers/analysis, Neurology/methods, Biomarkers, Pharmacological, topological data analysis, Diffusion, Alzheimer Disease, phenotype–brain mapping, Diffusion Tensor Imaging/psychology, manifold learning, Neuroimaging/methods, Parkinson Disease/psychology, Neurology/classification, Brain Mapping, Stochastic Processes, Clinical Relevance, Consciousness/classification, Epilepsy, Functional Neuroimaging, Noetic Diffusion Theory, Signal Processing, Computer-Assisted, Parkinson Disease, Neurogeometry, Models, Theoretical, Neurology/statistics & numerical data, Nonlinear Dynamics, Neurology, Diffusion Models, Hallucinogens, Alzheimer, Dementia, dynamic functional connectivity, Sleep, geometric signatures of brain states
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