Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Phasor Networks: Connecting Conventional and Spiking Neural
  Networks   In this work, we extend standard neural networks by building upon an
assumption that neuronal activations correspond to the angle of a complex
number lying on the unit circle, or 'phasor.' Each layer in such a network
produces new activations by taking a weighted superposition of the previous
layer's phases and calculating the new phase value. This generalized
architecture allows models to reach high accuracy and carries the singular
advantage that mathematically equivalent versions of the network can be
executed with or without regard to a temporal variable. Importantly, the value
of a phase angle in the temporal domain can be sparsely represented by a
periodically repeating series of delta functions or 'spikes'. We demonstrate
the atemporal training of a phasor network on standard deep learning tasks and
show that these networks can then be executed in either the traditional
atemporal domain or spiking temporal domain with no conversion step needed.
This provides a novel basis for constructing deep networkswhich operate via
temporal, spike-based calculations suitable for neuromorphic computing
hardware.

4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin