Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Bayesian Ising Approximation for revealing the neural
  dictionary in songbirds   The problem of deciphering how low-level patterns (action potentials in the
brain, amino acids in a protein, etc.) drive high-level biological features
(sensorimotor behavior, enzymatic function) represents the central challenge of
quantitative biology. The lack of general methods for doing so from the size of
datasets that can be collected experimentally severely limits our understanding
of the biological world. For example, in neuroscience, some sensory and motor
codes have been shown to consist of precisely timed multi-spike patterns.
However, the combinatorial complexity of such pattern codes have precluded
development of methods for their comprehensive analysis. Thus, just as it is
hard to predict a protein's function based on its sequence, we still do not
understand how to accurately predict an organism's behavior based on neural
activity. Here we derive a method for solving this class of problems. We
demonstrate its utility in an application to neural data, detecting precisely
timed spike patterns that code for specific motor behaviors in a songbird vocal
system. Our method detects such codewords with an arbitrary number of spikes,
does so from small data sets, and accounts for dependencies in occurrences of
codewords. Detecting such dictionaries of important spike patterns --- rather
than merely identifying the timescale on which such patterns exist, as in some
prior approaches --- opens the door for understanding fine motor control and
the neural bases of sensorimotor learning in animals. For example, for the
first time, we identify differences in encoding motor exploration versus
typical behavior. Crucially, our method can be used not only for analysis of
neural systems, but also for understanding the structure of correlations in
other biological and nonbiological datasets.

11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
1
The categories of this paper are: cs