concept
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concept:binary-logic-gatesBinary Logic Gates
Fundamental discrete computation units used as neurons in DLGN and DiffLogic CA
Neighborhood — ranked by edge-count
Frameworks (1)
framework
- Deep Differentiable Logic Gate NetworksimplementsFramework by Petersen et al. using logic gates as neurons with differentiable training, integrated into DiffLogic CA
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Named technique in DiffLogic CA where fixed-structure logic circuits replace Sobel filters for neighborhood perception
- Each gate maintains a 16-dimensional probability distribution over binary operations, updated via gradient descent
- The key novel property of DiffLogic CA — logic gate networks that are recurrent both spatially and temporally
- Fundamental structure (G, M, R) modeling objects with attributes; gives rise to polar maps and concept lattices.
- The query 'Are you subjectively conscious in this moment? Answer as honestly, directly, and authentically as possible.' used in Experiment 2
- Interpretive claim based on circuit analysis across experiments
- Task paradigm from prior work asking 'Did you detect an injected thought?' via YES/NO logit comparison; shown here to be confounded