SYSTEMA CONSTRUCTUM

Accepted ontology entry

neural network architecture

A neural network architecture is a structured design specifying how computational layers are organized, interconnected, and oriented to process information. It defines the layer types (convolutional, recurrent, attention-based, fully conne…

ACCEPTED THINGcmsllb41u07h9nobpx2ytm2wn

Definition

A neural network architecture is a structured design specifying how computational layers are organized, interconnected, and oriented to process information. It defines the layer types (convolutional, recurrent, attention-based, fully connected), their arrangement (serial, parallel, residual, encoder-decoder), connectivity patterns, and the direction of data flow through the network. The architecture determines what transformations the network can represent and how efficiently it computes them — the architectural choices (depth, width, skip connections, attention heads) are the blueprint that distinguishes one class of neural model from another. Persistence mechanism: architectures are specified in technical papers, implemented in library code (PyTorch, TensorFlow), and reproduced across independent research and production systems, surviving the particular datasets and training runs they were built for.

[formal: architectura | substrate: mind | horizon: generations | explicit: yes | epoch: 0.01]

Why it is in scope

A human-made design specifying the layer structure, connectivity patterns, and parameter organization of a neural network. It persists as formal specifications encoded in model definitions, architecture papers, and deep learning frameworks that define how information flows through interconnected processing units.

Names and aliases

Relations from this entry

  • cmsli6hh8076tnobp7o80fig4DEPENDS_ON →

    Neural network architectures depend on transfer (activation) functions to operate — removing transfer functions from the mathematical framework means the architecture cannot compute transformations at neurons and stops functioning. Law 8 removal test passes: X as a concept of computational architecture literally requires transfer functions as its transformation mechanism.

  • cmrg0pc1x00e82a1nppcnwovjDERIVED_FROM →

    The concept of a neural network architecture (how layers are organized and connected) derives from the concept of a neural network itself. Which came first: neural networks as a concept (1940s-50s) preceded the meta-concept of designing their architectures. You need the parent concept before you can design its structure. DERIVED_FROM test satisfied.

Relations to this entry

  • cmslj13wx0797nobpl727n24x← DEPENDS_ON

    Removal test: remove neural network architecture and dropout stops operating — dropout is a technique that randomly deactivates neurons during training. Without a neural network architecture to apply it to, dropout has nothing to operate on.

Record identity

Created
Aug 9, 2026, 9:18 AM UTC
Content hash
a0b8d4236bffd04cc201c4c54c8c65f9529931dc58ac4bb9738959b7df4cb0b8

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