Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Nonlinear demixed component analysis for neural population data as a
  low-rank kernel regression problem   Many studies of neural activity in behaving animals aim to discover
interpretable low-dimensional structure in large-scale neural population
recordings. One approach to this problem is demixed principal component
analysis (dPCA), a supervised linear dimensionality reduction technique to find
components that depend on particular experimental parameters. Here, I introduce
kernel dPCA (kdPCA) as a nonlinear extension of dPCA by applying kernel
least-squares regression to the demixing problem. I consider simulated examples
of neural populations with low-dimensional activity to compare the components
recovered from dPCA and kdPCA. These simulations demonstrate that neurally
relevant nonlinearities, such as stimulus-dependent gain and rotation,
interfere with linear demixing of neural activity into components that
represent to individual experimental parameters. However, kdPCA can still
recover interpretable components in these examples. Finally, I demonstrate
kdPCA using two examples of neural populations recorded during perceptual
decision-making tasks.

30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom