Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-supervised learning combining backpropagation and STDP: STDP
  enhances learning by backpropagation with a small amount of labeled data in a
  spiking neural network   A semi-supervised learning method for spiking neural networks is proposed.
The proposed method consists of supervised learning by backpropagation and
subsequent unsupervised learning by spike-timing-dependent plasticity (STDP),
which is a biologically plausible learning rule. Numerical experiments show
that the proposed method improves the accuracy without additional labeling when
a small amount of labeled data is used. This feature has not been achieved by
existing semi-supervised learning methods of discriminative models. It is
possible to implement the proposed learning method for event-driven systems.
Hence, it would be highly efficient in real-time problems if it were
implemented on neuromorphic hardware. The results suggest that STDP plays an
important role other than self-organization when applied after supervised
learning, which differs from the previous method of using STDP as pre-training
interpreted as self-organization.

25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio