Spike Triggered Covariance in Strongly Correlated Gaussian Stimuli

7Citations
Citations of this article
57Readers
Mendeley users who have this article in their library.

Abstract

Many biological systems perform computations on inputs that have very large dimensionality. Determining the relevant input combinations for a particular computation is often key to understanding its function. A common way to find the relevant input dimensions is to examine the difference in variance between the input distribution and the distribution of inputs associated with certain outputs. In systems neuroscience, the corresponding method is known as spike-triggered covariance (STC). This method has been highly successful in characterizing relevant input dimensions for neurons in a variety of sensory systems. So far, most studies used the STC method with weakly correlated Gaussian inputs. However, it is also important to use this method with inputs that have long range correlations typical of the natural sensory environment. In such cases, the stimulus covariance matrix has one (or more) outstanding eigenvalues that cannot be easily equalized because of sampling variability. Such outstanding modes interfere with analyses of statistical significance of candidate input dimensions that modulate neuronal outputs. In many cases, these modes obscure the significant dimensions. We show that the sensitivity of the STC method in the regime of strongly correlated inputs can be improved by an order of magnitude or more. This can be done by evaluating the significance of dimensions in the subspace orthogonal to the outstanding mode(s). Analyzing the responses of retinal ganglion cells probed with 1/f Gaussian noise, we find that taking into account outstanding modes is crucial for recovering relevant input dimensions for these neurons. © 2013 Aljadeff et al.

Cite

CITATION STYLE

APA

Aljadeff, J., Segev, R., Berry, M. J., & Sharpee, T. O. (2013). Spike Triggered Covariance in Strongly Correlated Gaussian Stimuli. PLoS Computational Biology, 9(9). https://doi.org/10.1371/journal.pcbi.1003206

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free