When the neuron’s features space is in low-dimension, but not 1-dimension, then STA is not sufficient, since it recovers only a 1-dimensional subspace. Spike triggered covariance (STC) is an extension of STA that can consistently estimate filters of a multi-dimensional LNP model [Paninski 2003]. Let us denote the zero-mean stimulus distribution as $p(x)$, and the spike triggered distribution as $q(x)$. Then, STA is the mean of $\hat{q}(x)$ (empirical estimate of $q(x)$), and STC is the eigen-vectors of the covariance matrix of $\hat{q}(x)$. STC is only a consistent estimator when the stimulus distribution is Gaussian [for details, see Paninski 2003].