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  "id": "oai:papers.qnfo.org:quantitative-simulation-of-the-brains-cognitive-architecture",
  "slug": "quantitative-simulation-of-the-brains-cognitive-architecture",
  "title": "Quantitative Simulation of the Brain's Cognitive Architecture",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
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  "abstract": "The human brain seamlessly integrates noisy local inputs into a globally consistent percept, a phenomenon that Euclidean models struggle to adequately explain. Theoretical physics suggests that frustrated complex systems settle into hierarchical equilibria characterized by ultrametric topologies. This paper proposes that the brain functions as a macroscopic algebraic solver of the cocycle condition, maintaining global topological consistency across a Bruhat-Tits tree. To explore this theoretical architecture, we utilized Python-based computational simulations to generate synthetic neural replicas. By extracting overlap matrices from these simulated macrostates, we analyzed the probability distributions of triangle inequality metrics to compare hierarchical models against Euclidean baselines. Our analysis revealed that the hierarchical model produced distance distributions characteristic of non-trivial isosceles triangles (mean base ratio = 0.155), whereas Euclidean baselines produced trivially equilateral distributions (mean base ratio = 0.058). A Mann-Whitney U test confirmed these distributions are fundamentally distinct (p < 0.001). [...]",
  "abstract_markdown": "The human brain seamlessly integrates noisy local inputs into a globally consistent percept, a phenomenon that Euclidean models struggle to adequately explain. Theoretical physics suggests that frustrated complex systems settle into hierarchical equilibria characterized by ultrametric topologies. This paper proposes that the brain functions as a macroscopic algebraic solver of the cocycle condition, maintaining global topological consistency across a Bruhat-Tits tree. To explore this theoretical architecture, we utilized Python-based computational simulations to generate synthetic neural replicas. By extracting overlap matrices from these simulated macrostates, we analyzed the probability distributions of triangle inequality metrics to compare hierarchical models against Euclidean baselines. Our analysis revealed that the hierarchical model produced distance distributions characteristic of non-trivial isosceles triangles (mean base ratio = 0.155), whereas Euclidean baselines produced trivially equilateral distributions (mean base ratio = 0.058). A Mann-Whitney U test confirmed these distributions are fundamentally distinct (\\(p < 0.001\\)). [...]",
  "date": "2026-04-01",
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        "name": "Rowan Brad Quni-Gudzinas",
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        "description": "The human brain seamlessly integrates noisy local inputs into a globally consistent percept, a phenomenon that Euclidean models struggle to adequately explain. Theoretical physics suggests that frustrated complex systems settle into hierarchical equilibria characterized by ultrametric topologies. This paper proposes that the brain functions as a macroscopic algebraic solver of the cocycle condition, maintaining global topological consistency across a Bruhat-Tits tree. To explore this theoretical architecture, we utilized Python-based computational simulations to generate synthetic neural replicas. By extracting overlap matrices from these simulated macrostates, we analyzed the probability distributions of triangle inequality metrics to compare hierarchical models against Euclidean baselines. Our analysis revealed that the hierarchical model produced distance distributions characteristic of non-trivial isosceles triangles (mean base ratio = 0.155), whereas Euclidean baselines produced trivially equilateral distributions (mean base ratio = 0.058). A Mann-Whitney U test confirmed these distributions are fundamentally distinct (p < 0.001). [...]",
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