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With our ability to record more neurons simultaneously, making sense of thesedata is a challenge. Functional connectivity is one popular way to study therelationship between multiple neural signals. Correlation-based methods are aset of currently well-used techniques for functional connectivity estimation.However, due to explaining away and unobserved common inputs (Stevenson et al.,2008), they produce spurious connections. The general linear model (GLM), whichmodels spikes trains as Poisson processes (Okatan et al., 2005; Truccolo etal., 2005; Pillow et al., 2008), avoids these confounds. We develop here a newclass of methods by using differential signals based on simulated intracellularvoltage recordings. It is equivalent to a regularized AR(2) model. We alsoexpand the method to simulated local field potential (LFP) recordings andcalcium imaging. In all of our simulated data, the differentialcovariance-based methods achieved better or similar performance to the GLMmethod and required fewer data samples. This new class of methods providesalternative ways to analyze neural signals.

Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings