This title appears in the Scientific Report :
2013
Please use the identifier:
http://dx.doi.org/10.3389/fncom.2010.00016 in citations.
Please use the identifier: http://hdl.handle.net/2128/5360 in citations.
Higher-order correlations in non-stationary parallel spike trains: statistical modeling and inference
Higher-order correlations in non-stationary parallel spike trains: statistical modeling and inference
The extent to which groups of neurons exhibit higher-order correlations in their spiking activity is a controversial issue in current brain research. A major difficulty is that currently available tools for the analysis of massively parallel spike trains (N >10) for higher-order correlations typi...
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Personal Name(s): | Staude, Benjamin (Corresponding author) |
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Grün, Sonja / Rotter, S. | |
Contributing Institute: |
Computational and Systems Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
Published in: | Frontiers in computational neuroscience, 4 16, S. 1-17 |
Imprint: |
Lausanne
Frontiers Research Foundation
2010
|
DOI: |
10.3389/fncom.2010.00016 |
Document Type: |
Journal Article |
Research Program: |
Helmholtz Alliance on Systems Biology Signalling Pathways and Mechanisms in the Nervous System |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/5360 in citations.
The extent to which groups of neurons exhibit higher-order correlations in their spiking activity is a controversial issue in current brain research. A major difficulty is that currently available tools for the analysis of massively parallel spike trains (N >10) for higher-order correlations typically require vast sample sizes. While multiple single-cell recordings become increasingly available, experimental approaches to investigate the role of higher-order correlations suffer from the limitations of available analysis techniques. We have recently presented a novel method for cumulant-based inference of higher-order correlations (CuBIC) that detects correlations of higher order even from relatively short data stretches of length T = 10–100 s. CuBIC employs the compound Poisson process (CPP) as a statistical model for the population spike counts, and assumes spike trains to be stationary in the analyzed data stretch. In the present study, we describe a non-stationary version of the CPP by decoupling the correlation structure from the spiking intensity of the population. This allows us to adapt CuBIC to time-varying firing rates. Numerical simulations reveal that the adaptation corrects for false positive inference of correlations in data with pure rate co-variation, while allowing for temporal variations of the firing rates has a surprisingly small effect on CuBICs sensitivity for correlations. |