The huge chemical complexity within each synapse suggests that neural models of cognition that refer only to changing weights of synaptic connections and ignore sub-neural chemistry are probably ignoring some of the most important explanatory mechanisms in brains.

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• If sub-neural chemical dynamics are crucial for cognition, then neural models ignoring them will fail to capture key cognitive phenomena.hypothesis0.891
  Sloman's implicit hypothesis behind his critique of synaptic weight-only models.
• Sub-Neural Chemistry in Synapsesconcept0.803
  The complex chemical processes within synapses, which Sloman argues are ignored by simplistic neural models focusing only on connection weights.
• The decoupling of the material state (protein content) from the information content is the first step to the most amazing aspect of neural networks: they enable mind to arise from matter.claim0.802
  Argues for a hardware/software distinction enabling emergence of mind.
• Connectionist models of cognition and learning identify conditions where collective intelligence can arise bottom-up, using only distributed learning mechanisms without system-level or global feedback.claim0.801
  Central claim about the power of connectionism.
• Cognition and learning are substrate-independent; principles familiar in artificial neural networks can be implemented by any network of signals with suitable weighted inputs.claim0.798
  Central thesis enabling unification of neural, developmental, ecological, and social networks as instances of collective intelligence.
• Split-brain patients and other aspects of cognitive neuroscience demonstrate that higher-level cognitive processes lack infallible access to lower-level processes and construct plausible post-hoc explanationsfinding0.796
  Cited as empirical evidence that confabulation is universal in biological cognition, not AI-specific
• The conservation of molecular mechanisms between neural and non-neural bioelectricity is why neuroscience tools work everywhere in the body.claim0.793
  Empirical claim about transferability of methods.
• The ability to represent relationships (e.g. correlations or associations) among variables, rather than a system of independent variables, is essential for any connectionist model of cognition.claim0.789
  Necessary condition for connectionist cognition.