These connections can be unidirectional, creating a recurrent network.
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It has been shown that a Recurrent networks have also been used for system identification.
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They are often implemented as recurrent networks.
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Like humans, the pseudo recurrent network showed a more gradual forgetting when to be trained list is lengthened.
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When given an input ( and a teacher value ) is fed into the pseudo-recurrent network would act as follows:
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When the activation patterns of the pseudo-recurrent network were investigated, it was shown that this network automatically formed semi-distributed representations.
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Choose an encoding for the lengths, train some sort of recurrent network on the data you have, and then get it to generate predictions.
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Recurrent networks are trained by unfolding them into very deep feedforward networks, where a new layer is created for each time step of an input sequence processed by the network.
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When tested on sequential learning of real world patterns, categorization of edible and poisonous mushrooms, the pseudo-recurrent network was shown less interference than a standard backpropagation network.
10.
In the pseudo-recurrent network, one of the sub-networks acts as an early processing area, akin to the hippocampus, and functions to learn new input patters.
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