|
gptkbp:instanceOf
|
recurrent neural network
|
|
gptkbp:activatedBy
|
sign function
|
|
gptkbp:capacity
|
approximately 0.15N for N neurons
|
|
gptkbp:energyFunction
|
gptkb:Lyapunov_function
|
|
gptkbp:field
|
gptkb:artificial_intelligence
gptkb:machine_learning
computational neuroscience
theoretical neuroscience
|
|
gptkbp:form
|
gptkb:discrete-time_dynamical_system
continuous-time dynamical system
|
|
gptkbp:generalizes
|
gptkb:Little-Hopfield_network
gptkb:continuous_Hopfield_network
gptkb:stochastic_Hopfield_network
|
|
gptkbp:hardware
|
digital circuits
analog circuits
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Hopfield network
|
|
gptkbp:influenced
|
modern neural networks
optimization algorithms
associative memory models
|
|
gptkbp:introducedIn
|
1982
|
|
gptkbp:limitation
|
cannot store correlated patterns well
limited storage capacity
spurious states
|
|
gptkbp:namedAfter
|
gptkb:John_Hopfield
|
|
gptkbp:neuronsAre
|
binary
|
|
gptkbp:relatedTo
|
gptkb:Ising_model
Boltzmann machine
content-addressable memory
|
|
gptkbp:roadType
|
fully connected
|
|
gptkbp:state
|
stable states correspond to local minima of energy
|
|
gptkbp:trainingRule
|
gptkb:Hebbian_learning
|
|
gptkbp:updateRule
|
asynchronous
synchronous
|
|
gptkbp:usedFor
|
optimization
associative memory
|
|
gptkbp:bfsParent
|
gptkb:Energy-based_models_in_machine_learning
gptkb:convolutional_neural_network
gptkb:Boltzmann_machine
|
|
gptkbp:bfsLayer
|
5
|