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439 (learning rule for tau_f) and 442 (learning rule for w).
Hebbian learning rule is used for network training.
A standard anti-Hebbian learning rule was used to decorrelate the two.
These results could be used to train the neural networks adapted back-propagation learning rule.
A theoretical analysis has been performed to establish the convergence of the learning rule.
Finally, we generalize the learning rule to non-harmonic oscillators like relaxation oscillators and strange attractors.
Adding a learning rule on the links of the cognitive map allows to reinforce particular paths, and to forget others.
In a medium that prided itself on comforting audiences, Seinfeld's "no hugging, no learning" rule was positively hostile.
I will demonstrate that this adjustment can be carried out by a very simple learning rule.
Using this learning rule, an Online Meta-neuron based Learning Algorithm (OMLA) is presented for an evolving spiking neural classifier.
In addition, a generalized learning rule has also been proposed to ensure more thorough and explorative search.
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