Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spiking Neural Networks -- Part I: Detecting Spatial Patterns   Spiking Neural Networks (SNNs) are biologically inspired machine learning
models that build on dynamic neuronal models processing binary and sparse
spiking signals in an event-driven, online, fashion. SNNs can be implemented on
neuromorphic computing platforms that are emerging as energy-efficient
co-processors for learning and inference. This is the first of a series of
three papers that introduce SNNs to an audience of engineers by focusing on
models, algorithms, and applications. In this first paper, we first cover
neural models used for conventional Artificial Neural Networks (ANNs) and SNNs.
Then, we review learning algorithms and applications for SNNs that aim at
mimicking the functionality of ANNs by detecting or generating spatial patterns
in rate-encoded spiking signals. We specifically discuss ANN-to-SNN conversion
and neural sampling. Finally, we validate the capabilities of SNNs for
detecting and generating spatial patterns through experiments.

7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
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The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga