Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: S4NN: temporal backpropagation for spiking neural networks with one
  spike per neuron   We propose a new supervised learning rule for multilayer spiking neural
networks (SNNs) that use a form of temporal coding known as rank-order-coding.
With this coding scheme, all neurons fire exactly one spike per stimulus, but
the firing order carries information. In particular, in the readout layer, the
first neuron to fire determines the class of the stimulus. We derive a new
learning rule for this sort of network, named S4NN, akin to traditional error
backpropagation, yet based on latencies. We show how approximated error
gradients can be computed backward in a feedforward network with any number of
layers. This approach reaches state-of-the-art performance with supervised
multi fully-connected layer SNNs: test accuracy of 97.4% for the MNIST dataset,
and 99.2% for the Caltech Face/Motorbike dataset. Yet, the neuron model that we
use, non-leaky integrate-and-fire, is much simpler than the one used in all
previous works. The source codes of the proposed S4NN are publicly available at
https://github.com/SRKH/S4NN.

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The categories of this paper are: nucl-ex
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: econ
32
The categories of this paper are: atom-ph
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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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: eess
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The categories of this paper are: astro-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga