Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A characterization of the Edge of Criticality in Binary Echo State
  Networks   Echo State Networks (ESNs) are simplified recurrent neural network models
composed of a reservoir and a linear, trainable readout layer. The reservoir is
tunable by some hyper-parameters that control the network behaviour. ESNs are
known to be effective in solving tasks when configured on a region in
(hyper-)parameter space called \emph{Edge of Criticality} (EoC), where the
system is maximally sensitive to perturbations hence affecting its behaviour.
In this paper, we propose binary ESNs, which are architecturally equivalent to
standard ESNs but consider binary activation functions and binary recurrent
weights. For these networks, we derive a closed-form expression for the EoC in
the autonomous case and perform simulations in order to assess their behavior
in the case of noisy neurons and in the presence of a signal. We propose a
theoretical explanation for the fact that the variance of the input plays a
major role in characterizing the EoC.

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The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
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The categories of this paper are: eess
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci