Fetching the paper…
Reading the bibliography…
The visual system is hierarchically organized to process visual information in successive stages.
The most numerous ganglion cell type of the mouse retina is a selective feature detector
Yifeng Zhang, In-Jung Kim, Joshua R. Sanes, and Markus Meister · 1901
Earlier work this paper cites.
What the Frog’s Eye Tells the Frog’s Brain
J. Y. Lettvin, H. R. Maturana, W. S. McCulloch, and W. H. Pitts · 1959
Earlier work this paper cites.
Possible principles underlying the transformations of sensory messages
HB Barlow · 1961
Earlier work this paper cites.
Towards a theory of early visual processing
Joseph J. Atick and A. Norman Redlich · 1990
Earlier work this paper cites.
What does the retina know about natural scenes?
Joseph J. Atick and A. Norman Redlich · 1992
Earlier work this paper cites.
Cortical neurons: isolation of contrast gain control
Wilson S. Geisler and Duane G. Albrecht · 1992
Earlier work this paper cites.
Normalization of cell responses in cat striate cortex
D. J. Heeger · 1992
Earlier work this paper cites.
Eye, brain, and vision
David H. Hubel · 1995
Earlier work this paper cites.
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
B. A. Olshausen and D. J. Field · 1996
Earlier work this paper cites.
The ”independent components” of natural scenes are edge filters
A. J. Bell and T. J. Sejnowski · 1997
Earlier work this paper cites.
Sparse coding with an overcomplete basis set: a strategy employed by V1?
B. A. Olshausen and D. J. Field · 1997
Earlier work this paper cites.
A simple white noise analysis of neuronal light responses
E. J. Chichilnisky · 2001
Earlier work this paper cites.
The fundamental plan of the retina
Richard H. Masland · 2001
Earlier work this paper cites.
Synaptic energy efficiency in retinal processing
Benjamin T. Vincent and Roland J. Baddeley · 2003
Cited alongside, same era.
Origins of perception: retinal ganglion cell diversity and the creation of parallel visual pathways
Dennis Dacey · 2004
Cited alongside, same era.
Is the early visual system optimised to be energy efficient?
Benjamin T. Vincent, Roland J. Baddeley, Tom Troscianko, and Iain D. Gilchrist · 2005
Cited alongside, same era.
Spike-triggered neural characterization
Odelia Schwartz, Jonathan W. Pillow, Nicole C. Rust, and Eero P. Simoncelli · 2006
Cited alongside, same era.
Y-cell receptive field and collicular projection of parasol ganglion cells in macaque monkey retina
Joanna D. Crook, Beth B. Peterson, Orin S. Packer, Farrel R. Robinson, John B. Troy, and Dennis M. Dacey · 2008
Cited alongside, same era.
Computing linear approximations to nonlinear neuronal response
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Daniel L. K. Yamins, Ha Hong, Charles F. Cadieu, Ethan A. Solomon, Darren Seibert, and James J. DiCarlo · 2014
Later among the works it cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Later among the works it cites.
Relevant sparse codes with variational information bottleneck
Matthew Chalk, Olivier Marre, and Gasper Tkacik · 2016
Later among the works it cites.
How Deep is the Feature Analysis underlying Rapid Visual Categorization?
Sven Eberhardt, Jonah G Cader, and Thomas Serre · 2016
Later among the works it cites.
Deep Learning Models of the Retinal Response to Natural Scenes
Lane McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, and Stephen Baccus · 2016
Later among the works it cites.
Deep convolutional models improve predictions of macaque V1 responses to natural images
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Melinda E. Koelling and Duane Q. Nykamp · 2008
Cited alongside, same era.
Eye smarter than scientists believed: neural computations in circuits of the retina
Tim Gollisch and Markus Meister · 2009
Cited alongside, same era.
Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky · 2009
Cited alongside, same era.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Cited alongside, same era.
Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons
Yan Karklin and Eero P. Simoncelli · 2011
Cited alongside, same era.
Efficient coding of spatial information in the primate retina
Eizaburo Doi, Jeffrey L. Gauthier, Greg D. Field, Jonathon Shlens, Alexander Sher, Martin Greschner, Timothy A. Machado, Lauren H. Jepson, Keith Mathieson, Deborah E. Gunning, Alan M. Litke, Liam Paninski, E. J. Chichilnisky, and Eero P. Simoncelli · 2012
Cited alongside, same era.
The Retina Dissects the Visual Scene into Distinct Features
Botond Roska and Markus Meister · 2014
Cited alongside, same era.
Santiago A. Cadena, George H. Denfield, Edgar Y. Walker, Leon A. Gatys, Andreas S. Tolias, Matthias Bethge, and Alexander S. Ecker · 2017
Later among the works it cites.
Multiplexed computations in retinal ganglion cells of a single type
Stephane Deny, Ulisse Ferrari, Emilie Mace, Pierre Yger, Romain Caplette, Serge Picaud, Gašper Tkačik, and Olivier Marre · 2017
Later among the works it cites.
Toward a unified theory of efficient, predictive, and sparse coding
Matthew Chalk, Olivier Marre, and Gašper Tkačik · 2018
Later among the works it cites.
A Pixel-Encoder Retinal Ganglion Cell with Spatially Offset Excitatory and Inhibitory Receptive Fields
Keith P. Johnson, Lei Zhao, and Daniel Kerschensteiner · 2018
Later among the works it cites.
Inferring hidden structure in multilayered neural circuits
Niru Maheswaranathan, David B. Kastner, Stephen A. Baccus, and Surya Ganguli · 2018
Later among the works it cites.
The emergence of multiple retinal cell types through efficient coding of natural movies
Samuel A. Ocko, Jack Lindsey, Surya Ganguli, and Stephane Deny · 2018
Later among the works it cites.
Sensory cortex is optimized for prediction of future input, June 2018
Yosef Singer, Yayoi Teramoto, Ben DB Willmore, Jan WH Schnupp, Andrew J. King, and Nicol S. Harper · 2018
Later among the works it cites.