"the 'code' these algorithms are implemented by is 'neural code', which is written, somehow, as enormous, inscrutable matrices of parameters"

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Neural codeconcept0.835
  The model's parameters considered as the actual 'code' implementing its algorithms, as opposed to human-written code.
• Ultimately, we would like to understand neural networks well enough to be able to intentionally design them.quote0.770
  Vision statement in the conclusion.
• Can an interpretable symbolic algorithm be used to faithfully explain a complex neural network model?question0.760
  Framing question for the paper's research program.
• Neural networks, trained with different objectives on different data and modalities, are converging to a shared statistical model of reality in their representation spaces.quote0.759
  The paper's central thesis statement, presented prominently after the abstract
• The integration of differentiable logic gates and neural cellular automata is a potential step towards programmable matter — Computroniumclaim0.759
  Authors' broader vision claim linking their system to Toffoli and Margolus's programmable matter concept
• Cognition and learning are substrate-independent; principles familiar in artificial neural networks can be implemented by any network of signals with suitable weighted inputs.claim0.758
  Central thesis enabling unification of neural, developmental, ecological, and social networks as instances of collective intelligence.
• Software implementations for all of the models/behaviours presented are common for n = 2, and can be made very efficient for α_i that map many objects onto a much smaller set of object families.claim0.754
  Claim about current practical feasibility and efficiency of 2-way associative implementations.
• Neural networks show substantial alignment with biological representations in the brain, driven by shared task and data constraintsclaim0.753
  Extends convergence argument to brain-machine alignment