Functional Modeling of Neural Topology
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2026
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| _version_ | 1866901666819735552 |
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| author | Williams, Andy E |
| author_facet | Williams, Andy E |
| contents | <p>Cognitive differences across individuals and species are typically characterized through trait-based measurements---IQ scores, symptom inventories, or isolated functional capacities---that lack mechanistic continuity across development, evolution, and pathology. This paper proposes an alternative: cognition as adaptive network topology under feedback. We define a layered functional model of cognitive network growth in which the system is decomposed into three non-collapsible layers: a Governance Layer (active, multi-attractor, non-tree-like), a Growth Layer (tree-like under scalarized fitness), and a Topology Layer (the resulting network structure). Neurons serve as stable, addressable functional units; cognitive transitions correspond to directed edges with associated costs and strengths; and reasoning styles emerge from the interaction of operator families with governance meta-controllers. When governance temporarily collapses fitness dimensionality onto a scalar objective, growth dynamics follow tree-like adaptive laws, producing qualitatively distinct topologies: dense, locally-branched networks versus sparse, trunk-dominant architectures with long-range bridges. The framework explains evolutionary phase transitions, particularly the emergence of language as a bridging operator that dramatically reduces cross-domain construction costs. All claims are framed as falsifiable predictions about network morphology rather than clinical assertions, with explicit specification of the domain of validity for each modeling component.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18360817 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Functional Modeling of Neural Topology Williams, Andy E <p>Cognitive differences across individuals and species are typically characterized through trait-based measurements---IQ scores, symptom inventories, or isolated functional capacities---that lack mechanistic continuity across development, evolution, and pathology. This paper proposes an alternative: cognition as adaptive network topology under feedback. We define a layered functional model of cognitive network growth in which the system is decomposed into three non-collapsible layers: a Governance Layer (active, multi-attractor, non-tree-like), a Growth Layer (tree-like under scalarized fitness), and a Topology Layer (the resulting network structure). Neurons serve as stable, addressable functional units; cognitive transitions correspond to directed edges with associated costs and strengths; and reasoning styles emerge from the interaction of operator families with governance meta-controllers. When governance temporarily collapses fitness dimensionality onto a scalar objective, growth dynamics follow tree-like adaptive laws, producing qualitatively distinct topologies: dense, locally-branched networks versus sparse, trunk-dominant architectures with long-range bridges. The framework explains evolutionary phase transitions, particularly the emergence of language as a bridging operator that dramatically reduces cross-domain construction costs. All claims are framed as falsifiable predictions about network morphology rather than clinical assertions, with explicit specification of the domain of validity for each modeling component.</p> |
| title | Functional Modeling of Neural Topology |
| url | https://doi.org/10.5281/zenodo.18360817 |