Hebbian Learning

Principle that correlations strengthen connections; implements distributed learning in connectionist networks without centralized supervision.

Neighborhood — ranked by edge-count

framework

Hopfield Network
implements
A recurrent connectionist architecture that implements associative memory and distributed computation through symmetric weighted connections and Hebbian learning rules. The network converges to stable states through recurrent dynamics, enabling both memory retrieval and combinatorial problem-solving in a fully distributed manner.
Tolman-Eichenbaum Machine (TEM)
implements
Neuroscience model of hippocampal formation that the paper shows is mathematically equivalent to a transformer with recurrent position encodings.
Connectionist Models of Cognition and Learning
implements
Models where intelligence arises from organisation of connections between simple processing units, used as basis for evolutionary connectionism

concept

Connectionism
implements
Central framework proposing intelligence resides in organization of relationships between components, not in individual parts; used to unify individual and collective intelligence.

method

Molecular Hebbian learning
related_to
Unsupervised learning rule in molecular systems where species i,j with high co-localized concentrations strengthen their interaction strength through proximity-based ligation

2-hop · via this method's ideas

Where ideas in this method connect to the rest of the corpus — the same concept, an analogy, or a restatement elsewhere.

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.

Hebbian Plasticityconcept0.749
Associative learning rule; learning of likelihood matrix A is formally identical to Hebbian plasticity.
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.claim0.713
Describes the self-reinforcing nature of Hebbian learning in networks.
Perceptual Learningconcept0.713
Process of inferring causes of sensory information; unified with value learning as integral aspects of free energy minimization.
Learningconcept0.713
Inference of parameters encoding contingencies of the world (e.g., likelihood matrix A) at slower timescale than perception.
Associative Learningconcept0.711
Sentience criterion; capacity occurs even in gene regulatory networks and non-neural morphogenetic agents.
Epistemic Learningconcept0.705
Learning model parameters through curious, uncertainty-reducing behavior; reducing ignorance about contingencies
Q-learningmethod0.699
Model-free RL algorithm used in experimental comparison; employs ε-greedy exploration.
Hebbian Plasticity Updatemethod0.698
Synaptic update rule that is formally identical to associative learning; used for learning A.