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claim:neurons-can-correspond-to-interpretable-functional-roles-but-interpretations-in-terms-of-individual-neurons-are-unlikely-to-be-the-most-parsimonious

Neurons can correspond to interpretable functional roles but interpretations in terms of individual neurons are unlikely to be the most parsimonious

Claim from footnote 3, acknowledging neuron-level interpretability while arguing subcomponents are better.

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Communities (3)

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Collective intelligence & distributed cognition
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All minds are composites of parts; individual and collective intelligence unified under substrate-neutral principles.
Substrate-agnostic behavioral inference of cognition
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Cognition and sentience attributed solely via observable behavior, not neural substrate or species.
Substrate-independent consciousness & cognition
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Framework arguing consciousness mechanisms transcend neural implementation, applicable to diverse physical systems including molecular and cellular architectures.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

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

Arora et al. (2025) work on interpretable neuron functional rolesconcept0.815
Prior work cited as evidence that individual neurons can correspond to interpretable functional roles, though parameter-level interpretation is argued to be more parsimonious.
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.claim0.808
Paper explicitly identifies this as a current gap requiring alternative experimental approaches
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.quote0.798
Load-bearing theoretical claim providing the conceptual foundation for DAS.
Our SAEs' features are more interpretable than neurons.claim0.794
Automated interpretability and specificity ratings show SAE features are clearer than MLP neurons.
The deep insights of neurobiology have relevance far beyond neurons.claim0.782
Claim that principles of neurobiology apply to other tissues and systems.
Superposition hypothesis: neural networks represent more features than dimensions using almost-orthogonal directions.hypothesis0.782
Explanation for why dictionary learning can recover many more features than dimensions.
If different organizations of nervous tissue can achieve the same functions, the possibility that cognitive capacities, including sentience, can be achieved by other tissues should be considered.claim0.781
Conditional claim urging consideration of non-neural tissues for cognition.
Neural networks show substantial alignment with biological representations in the brain, driven by shared task and data constraintsclaim0.780
Extends convergence argument to brain-machine alignment