Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Excitable Networks for Finite State Computation with Continuous Time
  Recurrent Neural Networks   Continuous time recurrent neural networks (CTRNN) are systems of coupled
ordinary differential equations that are simple enough to be insightful for
describing learning and computation, from both biological and machine learning
viewpoints. We describe a direct constructive method of realising finite state
input-dependent computations on an arbitrary directed graph. The constructed
system has an excitable network attractor whose dynamics we illustrate with a
number of examples. The resulting CTRNN has intermittent dynamics: trajectories
spend long periods of time close to steady-state, with rapid transitions
between states. Depending on parameters, transitions between states can either
be excitable (inputs or noise needs to exceed a threshold to induce the
transition), or spontaneous (transitions occur without input or noise). In the
excitable case, we show the threshold for excitability can be made arbitrarily
sensitive.

30
The categories of this paper are: q-alg
1
The categories of this paper are: cs
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th