Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Spiking Neural Networks with Logarithmic Temporal Coding   A Spiking Neural Network (SNN) can be trained indirectly by first training an
Artificial Neural Network (ANN) with the conventional backpropagation
algorithm, then converting it into an SNN. The conventional rate-coding method
for SNNs uses the number of spikes to encode magnitude of an activation value,
and may be computationally inefficient due to the large number of spikes.
Temporal-coding is typically more efficient by leveraging the timing of spikes
to encode information. In this paper, we present Logarithmic Temporal Coding
(LTC), where the number of spikes used to encode an activation value grows
logarithmically with the activation value; and the accompanying
Exponentiate-and-Fire (EF) spiking neuron model, which only involves efficient
bit-shift and addition operations. Moreover, we improve the training process of
ANN to compensate for approximation errors due to LTC. Experimental results
indicate that the resulting SNN achieves competitive performance at
significantly lower computational cost than related work.

18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph