Learning the likelihood matrix A via Dirichlet parameter accumulation is formally identical to associative or Hebbian plasticity.

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

• Associative learning criterion can occur in gene regulatory networks and non-neural morphogenetic agentshypothesis0.765
• Dirichlet Parameter Accumulationmethod0.764
  Learning rule for updating Dirichlet beliefs about likelihood matrix A by adding outer products of observations and state estimates.
• The results of abductive reasoning (reduced model priors) can be communicated to other agents as prior beliefs, provided all agents share the same model lexicon or hypothesis space.claim0.756
  Explanation of how knowledge (not just parameters) is shared between agents; links to pre-Cartesian consciousness
• H2: The conditions necessary for the induction of deep models, familiar in connectionist models of learning and cognition, are predictive of the conditions necessary for an ETI to occur.hypothesis0.754
  Second hypothesis linking learning theory directly to evolutionary transitions
• The randomization of bioelectric state and downstream morphogen gradients is interpreted with respect to anterior-posterior organ identity by collectives, not by individual cells.claim0.753
  Argues that stochastic outcomes in regeneration are still collective decisions, not cellular chaos.
• Associative learning occurs in gene regulatory networks and other non-neural systems, making it substrate-independent.claim0.750
• Dense Associative Memory for Pattern Recognition (Krotov & Hopfield, 2016)concept0.749
  Paper exploring two-type feature binding in dense associative memory; analogous to TEM-t binding g and x in memory neurons.
• Hebbian learning in a self-modelling dynamical system effects a positive feedback on correlations; the more things co-occur, the more the connection between them changes to make them more likely to co-occur in future.claim0.749
  Describes the self-reinforcing nature of Hebbian learning in networks.