| Autor: |
Lin TW; Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A., and Neurosciences Graduate Program, University of California San Diego, La Jolla, CA 92092, U.S.A. wulin@ucsd.edu., Das A; Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A., and Jacobs School of Engineering, University of California San Diego, La Jolla, CA 92092, U.S.A. aldas@eng.ucsd.edu., Krishnan GP; Department of Medicine, University of California San Diego, La Jolla, CA 92092, U.S.A. giri.prashanth@gmail.com., Bazhenov M; Department of Medicine, University of California San Diego, La Jolla, CA 92092, U.S.A. maksimb@ucr.edu., Sejnowski TJ; Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A., and Institute for Neural Computation, University of California San Diego, La Jolla, CA 92092, U.S.A. terry@salk.edu. |
| Abstrakt: |
With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Körding, 2008 ), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005 ; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005 ; Pillow et al., 2008 ), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better than or similar to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals. |
