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Many visual and auditory neurons have response properties that are well explained by pooling the rectified responses of a set of selfsimilar linear filters. These filters cannot be found using spiketriggered averaging (STA), which estimates only a single filter. Other methods, like spiketriggered covariance (STC), define a multidimensional response subspace, but require substantial amounts of data and do not produce unique estimates of the linear filters. Rather, they provide a linear basis for the subspace in which the filters reside. Here, we define a subunit' model as an LNLN cascade, in which the first linear stage is restricted to a set of shifted (``convolutional’’) copies of a common filter, and the first nonlinear stage consists of rectifying nonlinearities that are identical for all filter outputs; we refer to these initial LN elements as the
subunits' of the receptive field. The second linear stage then computes a weighted sum of the responses of the rectified subunits. We present a method for directly fitting this model to spike data. The method performs well for both simulated and real data (from primate V1), and the resulting model outperforms STA and STC in terms of both crossvalidated accuracy and efficiency.
Author Information
Brett Vintch (iheartradio)
Andrew Zaharia (New York University)
J Movshon (New York University)
Eero Simoncelli (FlatIron Institute / New York University)
Eero P. Simoncelli received the B.S. degree in Physics in 1984 from Harvard University, studied applied mathematics at Cambridge University for a year and a half, and then received the M.S. degree in 1988 and the Ph.D. degree in 1993, both in Electrical Engineering from the Massachusetts Institute of Technology. He was an Assistant Professor in the Computer and Information Science department at the University of Pennsylvania from 1993 until 1996. He moved to New York University in September of 1996, where he is currently a Professor in Neural Science, Mathematics, and Psychology. In August 2000, he became an Associate Investigator of the Howard Hughes Medical Institute, under their new program in Computational Biology. In Fall 2020, he resigned his HHMI appointment to become the scientific director of the Center for Computational Neuroscience at the Flatiron Institute, of the Simons Foundation. His research interests span a wide range of topics in the representation and analysis of visual images, in both machine and biological systems.
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