Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deciphering subsampled data: adaptive compressive sampling as a
  principle of brain communication   A new algorithm is proposed for a) unsupervised learning of sparse
representations from subsampled measurements and b) estimating the parameters
required for linearly reconstructing signals from the sparse codes. We verify
that the new algorithm performs efficient data compression on par with the
recent method of compressive sampling. Further, we demonstrate that the
algorithm performs robustly when stacked in several stages or when applied in
undercomplete or overcomplete situations. The new algorithm can explain how
neural populations in the brain that receive subsampled input through fiber
bottlenecks are able to form coherent response properties.

13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph