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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 self-similar linear filters. These filters cannot be found using spike-triggered averaging (STA), which estimates only a single filter. Other methods, like spike-triggered covariance (STC), define a multi-dimensional 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 LN-LN 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 cross-validated 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. 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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2020 Poster: Learning efficient task-dependent representations with synaptic plasticity »
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2019 Poster: Flexible information routing in neural populations through stochastic comodulation »
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2017 Poster: Eigen-Distortions of Hierarchical Representations »
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2017 Oral: Eigen-Distortions of Hierarchical Representations »
Alexander Berardino · Valero Laparra · Johannes Ballé · Eero Simoncelli -
2012 Poster: Hierarchical spike coding of sound »
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2012 Spotlight: Hierarchical spike coding of sound »
yan karklin · Chaitanya Ekanadham · Eero Simoncelli -
2011 Poster: Efficient coding with a population of Linear-Nonlinear neurons »
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2011 Poster: A blind sparse deconvolution method for neural spike identification »
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2011 Spotlight: A blind sparse deconvolution method for neural spike identification »
Chaitanya Ekanadham · Daniel Tranchina · Eero Simoncelli -
2010 Poster: Implicit encoding of prior probabilities in optimal neural populations »
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2009 Poster: Hierarchical Modeling of Local Image Features through $L_p$-Nested Symmetric Distributions »
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2008 Oral: Reducing statistical dependencies in natural signals using radial Gaussianization »
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2008 Poster: Reducing statistical dependencies in natural signals using radial Gaussianization »
Siwei Lyu · Eero Simoncelli -
2008 Tutorial: Statistical Models of Visual Images »
Eero Simoncelli -
2007 Poster: A Bayesian Model of Conditioned Perception »
Alan A Stocker · Eero Simoncelli -
2006 Poster: Statistical Modeling of Images with Fields of Gaussian Scale Mixtures »
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2006 Poster: Learning to be Bayesian without Supervision »
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