Subunit models provide a powerful yet parsimonious description of neural spike responses to complex stimuli. They can be expressed by a cascade of two linear- ...

Here we address this problem by providing a theoretical connection between spike-triggered covariance analy- sis and nonlinear subunit models. Specifically, we ...

Dec 7, 2015 · They are defined by a cascade of two linear-nonlinear (LN) stages, with the first stage defined by a linear convolution with one or more filters ...

May 23, 2016 · Here we address this problem by providing a theoretical connection between spike-triggered covariance analysis and nonlinear subunit models.

Specifically, we show that a "convolutional" decomposition of a spike-triggered average (STA) and covariance (STC) matrix provides an asymptotically efficient ...

Jan 21, 2021 · Bibliographic details on Convolutional spike-triggered covariance analysis for neural subunit models.

Moment based estimator for subunit model described in Wu et.al, 2015. Convolutional spike-triggered covariance analysis for neural subunit models.

This work provides a theoretical connection between spike-triggered covariance analysis and nonlinear subunit models, by showing that a “convolutional” ...

Standard methods that are often used to characterize multidimensional stimulus selectivity, such as spike-triggered covariance (STC) or maximally informative ...

Nov 4, 2015 · Subspace methods like spike-triggered covariance can recover multiple filters but require substantial amounts of data, and recover an orthogonal ...