Molecular Hebbian learning

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

Neighborhood — ranked by edge-count

Papers (1)

paper

Learning without neurons in physical systems
introduces

Methods (1)

method

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

Hypotheses (1)

hypothesis

Collective nucleation dynamics in Hebbian-learned molecular interaction systems can perform pattern recognition by assembling different structures in response to different concentration patterns
supports
Theoretical prediction that molecular systems with proximity-based learning can recognize patterns; has mathematical connections to Hopfield associative memory

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.

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.claim0.763
Describes the self-reinforcing nature of Hebbian learning in networks.
Epistemic Learningconcept0.731
Learning model parameters through curious, uncertainty-reducing behavior; reducing ignorance about contingencies
Hebbian Plasticityconcept0.724
Associative learning rule; learning of likelihood matrix A is formally identical to Hebbian plasticity.
Gene regulatory networks may exhibit learning capacity.claim0.717
Recent models show GRNs can perform associative learning and pattern completion.
Meta-learningconcept0.713
The capability of GPT-3 to learn tasks from few-shot prompts during runtime.
Connectionist Models of Cognition and Learningframework0.709
Models where intelligence arises from organisation of connections between simple processing units, used as basis for evolutionary connectionism
Learningconcept0.708
Inference of parameters encoding contingencies of the world (e.g., likelihood matrix A) at slower timescale than perception.
manifold learningframework0.707
Technique used to fit M_h and M_y from data; enables manifold steering.