18 Facts About Hopfield network

1.

Hopfield network is a form of recurrent artificial neural network and a type of spin glass system popularised by John Hopfield in 1982 as described earlier by Little in 1974 based on Ernst Ising's work with Wilhelm Lenz on the Ising model.

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2.

Units in Hopfield nets are binary threshold units, i e the units only take on two different values for their states, and the value is determined by whether or not the unit's input exceeds its threshold.

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3.

Discrete Hopfield nets describe relationships between binary neurons.

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4.

Hopfield network modeled neural nets for continuous values, in which the electric output of each neuron is not binary but some value between 0 and 1.

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5.

Hopfield network found that this type of network was able to store and reproduce memorized states.

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6.

Notice that every pair of units i and j in a Hopfield network has a connection that is described by the connectivity weight.

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7.

Furthermore, under repeated updating the Hopfield network will eventually converge to a state which is a local minimum in the energy function.

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8.

Convergence is generally assured, as Hopfield network proved that the attractors of this nonlinear dynamical system are stable, not periodic or chaotic as in some other systems.

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9.

Training a Hopfield network net involves lowering the energy of states that the net should "remember".

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10.

For example, if we train a Hopfield net with five units so that the state is an energy minimum, and we give the network the state it will converge to.

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11.

Storkey showed that a Hopfield network trained using this rule has a greater capacity than a corresponding network trained using the Hebbian rule.

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12.

The weight matrix of an attractor neural Hopfield network is said to follow the Storkey learning rule if it obeys:.

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13.

Network capacity of the Hopfield network model is determined by neuron amounts and connections within a given network.

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14.

Therefore, the Hopfield network model is shown to confuse one stored item with that of another upon retrieval.

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15.

Ulterior models inspired by the Hopfield network were later devised to raise the storage limit and reduce the retrieval error rate, with some being capable of one-shot learning.

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16.

The entire Hopfield network contributes to the change in the activation of any single node.

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17.

The Hopfield network still requires a sufficient number of hidden neurons.

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18.

Simple example of the modern Hopfield network can be written in terms of binary variables that represent the active and inactive state of the model neuron.

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