Connection weight although added to the network data record was never implemented.
2.
The solution produced initially with the stimulus and association connection weights is likely to be discrepant resulting in a relatively large error.
3.
In connectionist systems, representations are distributed across all the nodes and connection weights of the system and thus are said to be sub symbolic.
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However, this discrepant result can be used to modify the connection weights thereby leading to a more accurate solution and a smaller error term.
5.
The original paper presented a perceptron network whose connection weights were trained with the back-propagation algorithm, this may be done in batch or online.
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Using the NEF, Nengo allows defining connection weights between populations of spiking neurons by specifying the function to be computed, instead of forcing the weights to be set manually, or use a learning rule to configure them from a random start.
7.
One common distinction is whether algorithms evolve only the strength of the connection weights for a fixed network topology ( sometimes called conventional neuroevolution ), as opposed to those that evolve both the topology of the network and its weights ( called TWEANNs, for Topology & Weight Evolving Artificial Neural Network algorithms ).
8.
McCloskey and Cohen tried to reduce interference through a number of manipulations including changing the number of hidden units, changing the value of the learning rate parameter, overtraining on the A-B list, freezing certain connection weights, changing target values 0 and 1 instead 0.1 and 0.9 . However none of these manipulations satisfactorily reduced the catastrophic interference exhibited by the networks.
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