Efficient and direct estimation of a neural subunit model for sensory coding Brett Vintch Andrew D. Zaharia J. Anthony Movshon Eero P. Simoncelli

Many visual and auditory neurons have response properties that are well explained by pooling the rectified responses of a set of spatially shifted linear filters. These filters cannot be estimated using spike-triggered averaging (STA). Subspace methods such as spike-triggered covariance (STC) can recover multiple filters, but require substantial amounts of data, and recover an orthogonal basis for the subspace in which the filters reside rather than the filters themselves.