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Efficient coding provides a powerful principle for explaining early sensory coding. Most attempts to test this principle have been limited to linear, noiseless models, and when applied to natural images, have yielded oriented filters consistent with responses in primary visual cortex. Here we show that an efficient coding model that incorporates biologically realistic ingredients -- input and output noise, nonlinear response functions, and a metabolic cost on the firing rate -- predicts receptive fields and response nonlinearities similar to those observed in the retina. Specifically, we develop numerical methods for simultaneously learning the linear filters and response nonlinearities of a population of model neurons, so as to maximize information transmission subject to metabolic costs. When applied to an ensemble of natural images, the method yields filters that are center-surround and nonlinearities that are rectifying. The filters are organized into two populations, with On- and Off-centers, which independently tile the visual space. As observed in the primate retina, the Off-center neurons are more numerous and have filters with smaller spatial extent. In the absence of noise, our method reduces to a generalized version of independent components analysis, with an adapted nonlinear ``contrast'' function; in this case, the optimal filters are localized and oriented.
Author Information
yan karklin (Knewton)
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 BallĂ© · Eero Simoncelli -
2012 Poster: Efficient and direct estimation of a neural subunit model for sensory coding »
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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: 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 »
Siwei Lyu · Eero Simoncelli -
2006 Poster: Learning to be Bayesian without Supervision »
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