Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High-parallelism Inception-like Spiking Neural Networks for Unsupervised
  Feature Learning   Spiking Neural Networks (SNNs) are brain-inspired, event-driven machine
learning algorithms that have been widely recognized in producing
ultra-high-energy-efficient hardware. Among existing SNNs, unsupervised SNNs
based on synaptic plasticity, especially Spike-Timing-Dependent Plasticity
(STDP), are considered to have great potential in imitating the learning
process of the biological brain. Nevertheless, the existing STDP-based SNNs
have limitations in constrained learning capability and/or slow learning speed.
Most STDP-based SNNs adopted a slow-learning Fully-Connected (FC) architecture
and used a sub-optimal vote-based scheme for spike decoding. In this paper, we
overcome these limitations with: 1) a design of high-parallelism network
architecture, inspired by the Inception module in Artificial Neural Networks
(ANNs); 2) use of a Vote-for-All (VFA) decoding layer as a replacement to the
standard vote-based spike decoding scheme, to reduce the information loss in
spike decoding and, 3) a proposed adaptive repolarization (resetting) mechanism
that accelerates SNNs' learning by enhancing spiking activities. Our
experimental results on two established benchmark datasets (MNIST/EMNIST) show
that our network architecture resulted in superior performance compared to the
widely used FC architecture and a more advanced Locally-Connected (LC)
architecture, and that our SNN achieved competitive results with
state-of-the-art unsupervised SNNs (95.64%/80.11% accuracy on the MNIST/EMNISE
dataset) while having superior learning efficiency and robustness against
hardware damage. Our SNN achieved great classification accuracy with only
hundreds of training iterations, and random destruction of large numbers of
synapses or neurons only led to negligible performance degradation.

7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin