These connections can be unidirectional, creating a recurrent network.
2.
It has been shown that a Recurrent networks have also been used for system identification.
3.
They are often implemented as recurrent networks.
4.
Like humans, the pseudo recurrent network showed a more gradual forgetting when to be trained list is lengthened.
5.
When given an input ( and a teacher value ) is fed into the pseudo-recurrent network would act as follows:
6.
When the activation patterns of the pseudo-recurrent network were investigated, it was shown that this network automatically formed semi-distributed representations.
7.
Choose an encoding for the lengths, train some sort of recurrent network on the data you have, and then get it to generate predictions.
8.
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.
9.
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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