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They can additionally store approximately one real number from their input history per hidden unit.
Two potential bottlenecks on the expressiveness of recurrent neural networks (RNNs) are their ability to store information about the task in their parameters, and to store information about the input history in their units.
The hidden state is a function of the entire input history.
From the perspective of dynamic system theory, these states of hidden neuron nodes serve as a memory of the input audio feature and are termed as echoes of the input history.
Indeed, a neuron model with a multiple time-scale current efficiently encodes input history over several time-scales [55].
Systems whose output depends not only on the current inputs but the input history are necessary for sophisticated computation and information storage.
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Given the fact that time history signals recorded at a given site constitute a random process which is practically impossible to reproduce, considerable effort has been expended in recent years on processing actual records so as to become 'representative' of future input histories to existing as well as planned construction in earthquake-prone regions.
A generator is a function returning sound histories from input histories, which is a much more complicated object than a single sound history, a sample.
The adoption of linear models guarantees that the presented results also hold if the input histories include fluctuating currents ("frozen noise"), as long as they are equal among different input histories.
The discriminability between two input histories aims at measuring the difference in the corresponding evolutions of the HDE.
Another important difference between oscillating and non-oscillating neurons regards the sensitivity to small differences in the input histories.
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