Sub-Neural Chemistry in Synapses

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

• 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.claim0.803
  Sloman's critique of mainstream neural network theories.
• Synaptic Homeostasisconcept0.761
  Biological mechanism of sleep-dependent synapse elimination; analogous to Bayesian model reduction removing redundant parameters
• If sub-neural chemical dynamics are crucial for cognition, then neural models ignoring them will fail to capture key cognitive phenomena.hypothesis0.760
  Sloman's implicit hypothesis behind his critique of synaptic weight-only models.
• Synaptic Plasticityconcept0.747
  Experience-dependent changes in synaptic weights, implementing learning of A, B, D matrices.
• neural cognitionconcept0.736
  Cognition in nervous systems, used as a modelling target
• neural substratesconcept0.728
  Brain-based physical implementations of consciousness-related functions, assumed by many ToCs to be exclusive.
• All neuronal processing and action selection minimize variational free energy, unifying perception, action, and learning.claim0.723
  Fundamental assertion: single imperative (free energy minimization) explains diverse cognitive and neural phenomena.
• Investigating the causal substructure of neural representations is necessary to avoid misidentifying data structures of simpler representations as abstract conceptsclaim0.719
  Motivated by the finding that lexical entailment decomposes into word identities.