Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Spiking Networks   We introduce an algorithm to do backpropagation on a spiking network. Our
network is "spiking" in the sense that our neurons accumulate their activation
into a potential over time, and only send out a signal (a "spike") when this
potential crosses a threshold and the neuron is reset. Neurons only update
their states when receiving signals from other neurons. Total computation of
the network thus scales with the number of spikes caused by an input rather
than network size. We show that the spiking Multi-Layer Perceptron behaves
identically, during both prediction and training, to a conventional deep
network of rectified-linear units, in the limiting case where we run the
spiking network for a long time. We apply this architecture to a conventional
classification problem (MNIST) and achieve performance very close to that of a
conventional Multi-Layer Perceptron with the same architecture. Our network is
a natural architecture for learning based on streaming event-based data, and is
a stepping stone towards using spiking neural networks to learn efficiently on
streaming data.

26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an