Hopfield networks

E46142

associative memory model content-addressable memory system recurrent artificial neural network

Hopfield networks are recurrent artificial neural networks that serve as content-addressable memory systems, storing patterns as stable states and retrieving them through dynamics that minimize an energy function.

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All labels observed (5)

Label	Occurrences
Hopfield network	2
Hopfield networks canonical	2
Neural networks and physical systems with emergent collective computational abilities	1
continuous Hopfield network	1
modern Hopfield network	1

Statements (50)

Predicate	Object
instanceOf	associative memory model ⓘ content-addressable memory system ⓘ recurrent artificial neural network ⓘ
belongsToField	computational neuroscience ⓘ machine learning ⓘ neural networks ⓘ statistical physics ⓘ
convergesTo	local energy minima ⓘ
dynamicsMinimize	energy function ⓘ
hasActivationFunction	sign function ⓘ threshold function ⓘ
hasApproximateCapacity	0.138N for random uncorrelated patterns ⓘ
hasCapacityProperty	storage capacity proportional to number of neurons ⓘ
hasConnectionType	no self-connections ⓘ symmetric weights ⓘ
hasEnergyFunction	Lyapunov function ⓘ
hasLearningRule	Hebbian learning ⓘ outer-product rule ⓘ
hasLimitation	limited storage capacity ⓘ sensitivity to correlated patterns ⓘ spurious attractors ⓘ
hasMathematicalRepresentation	binary quadratic form energy ⓘ
hasNodeType	Ising spin ⓘ binary neuron ⓘ
hasProperty	guaranteed convergence under symmetric weights and asynchronous updates ⓘ
hasStateSpace	binary vectors ⓘ
hasTopology	fully connected network ⓘ
hasUpdateDynamics	deterministic dynamics ⓘ
hasUpdateRule	asynchronous update ⓘ synchronous update ⓘ
hasVariant	Hopfield networks self-linksurface differs ⓘ surface form: continuous Hopfield network Hopfield networks self-linksurface differs ⓘ surface form: modern Hopfield network stochastic Hopfield network ⓘ
introducedBy	John Hopfield ⓘ
introducedInYear	1982 ⓘ
isRelatedTo	Boltzmann machines ⓘ surface form: Boltzmann machine Ising models ⓘ surface form: Ising Hopfield model Ising models ⓘ surface form: Ising model spin glass theory ⓘ
isUsedFor	associative memory tasks ⓘ combinatorial optimization ⓘ constraint satisfaction ⓘ optimization ⓘ
namedAfter	John Hopfield ⓘ
stableStatesRepresent	stored patterns ⓘ
supports	associative recall ⓘ content-addressable memory ⓘ error correction ⓘ pattern completion ⓘ robust retrieval from noisy inputs ⓘ

Referenced by (7)

Full triples — surface form annotated when it differs from this entity's canonical label.

Boltzmann machines → relatedTo → Hopfield networks ⓘ

Hopfield networks → hasVariant → Hopfield networks self-linksurface differs ⓘ

subject surface form: Hopfield network

this entity surface form: continuous Hopfield network

Hopfield networks → hasVariant → Hopfield networks self-linksurface differs ⓘ

subject surface form: Hopfield network

this entity surface form: modern Hopfield network

Hebbian learning → usedIn → Hopfield networks ⓘ

John Hopfield → knownFor → Hopfield networks ⓘ

this entity surface form: Hopfield network

John Hopfield → notableWork → Hopfield networks ⓘ

this entity surface form: Neural networks and physical systems with emergent collective computational abilities

John Hopfield → developed → Hopfield networks ⓘ

this entity surface form: Hopfield network