The typical case is that neurons (or other directions in activation space) are understandable after thousands of hours of study, even when initially mysterious.

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

• Neuroscience and mechanistic interpretability have not yet made enough progress to identify neural correlates marking necessary and sufficient conditions of conscious experience in both brains and neural networks.claim0.794
  Paper explicitly identifies this as a current gap requiring alternative experimental approaches
• The remarkable ability of neurons to unify toward a centralized self is an evolutionary pivot of far earlier cell communication strategies that first solved problems in navigating anatomical morphospace.hypothesis0.774
  Proposes an evolutionary trajectory linking morphogenesis to neural cognition.
• Neurons can correspond to interpretable functional roles but interpretations in terms of individual neurons are unlikely to be the most parsimoniousclaim0.774
  Claim from footnote 3, acknowledging neuron-level interpretability while arguing subcomponents are better.
• Superposition hypothesis: neural networks represent more features than dimensions using almost-orthogonal directions.hypothesis0.773
  Explanation for why dictionary learning can recover many more features than dimensions.
• The deep insights of neurobiology have relevance far beyond neurons.claim0.770
  Claim that principles of neurobiology apply to other tissues and systems.
• We hypothesize that insight produces a profound reduction in the latency of evoked neuronal responses when subjects know the meaning of cues (have learned a rule), equivalently an increase in ERP amplitude to initial cues in a sequence.hypothesis0.769
  Empirically testable neural prediction of the active inference model of insight
• In the long run, studying circuit motifs may be more important than studying individual circuits for understanding neural networks.claim0.769
  Strategic claim about the relative importance of motif-level abstraction over circuit-level analysis
• Smolensky (1986) proposes that viewing a neural representation under a basis that is not aligned with individual neurons can reveal the interpretable distributed structure of the neural representations.quote0.768
  Load-bearing theoretical claim providing the conceptual foundation for DAS.