Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning with the Random Neural Network and its Applications   The random neural network (RNN) is a mathematical model for an "integrate and
fire" spiking network that closely resembles the stochastic behaviour of
neurons in mammalian brains. Since its proposal in 1989, there have been
numerous investigations into the RNN's applications and learning algorithms.
Deep learning (DL) has achieved great success in machine learning. Recently,
the properties of the RNN for DL have been investigated, in order to combine
their power. Recent results demonstrate that the gap between RNNs and DL can be
bridged and the DL tools based on the RNN are faster and can potentially be
used with less energy expenditure than existing methods.

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The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-th
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: atom-ph
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The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci