Literature DB >> 16306892

In praise of artifice.

Nicole C Rust1, J Anthony Movshon.   

Abstract

The visual system evolved to process natural images, and the goal of visual neuroscience is to understand the computations it uses to do this. Indeed the goal of any theory of visual function is a model that will predict responses to any stimulus, including natural scenes. It has, however, recently become common to take this fundamental principle one step further: trying to use photographic or cinematographic representations of natural scenes (natural stimuli) as primary probes to explore visual computations. This approach is both challenging and controversial, and we argue that this use of natural images is so fraught with difficulty that it is not useful. Traditional methods for exploring visual computations that use artificial stimuli with carefully selected properties have been and continue to be the most effective tools for visual neuroscience. The proper use of natural stimuli is to test models based on responses to these synthetic stimuli, not to replace them.

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Mesh:

Year:  2005        PMID: 16306892     DOI: 10.1038/nn1606

Source DB:  PubMed          Journal:  Nat Neurosci        ISSN: 1097-6256            Impact factor:   24.884


  71 in total

1.  Speed dependence of tuning to one-dimensional features in V1.

Authors:  Ferenc Mechler; Ifije E Ohiorhenuan; Jonathan D Victor
Journal:  J Neurophysiol       Date:  2007-01-24       Impact factor: 2.714

2.  Robust coding of flow-field parameters by axo-axonal gap junctions between fly visual interneurons.

Authors:  Hermann Cuntz; Juergen Haag; Friedrich Forstner; Idan Segev; Alexander Borst
Journal:  Proc Natl Acad Sci U S A       Date:  2007-06-05       Impact factor: 11.205

3.  On the importance of static nonlinearity in estimating spatiotemporal neural filters with natural stimuli.

Authors:  Tatyana O Sharpee; Kenneth D Miller; Michael P Stryker
Journal:  J Neurophysiol       Date:  2008-03-19       Impact factor: 2.714

4.  Adapting to altered image statistics using processed video.

Authors:  Michael Falconbridge; David Wozny; Ladan Shams; Stephen A Engel
Journal:  Vision Res       Date:  2009-04-11       Impact factor: 1.886

Review 5.  Computational identification of receptive fields.

Authors:  Tatyana O Sharpee
Journal:  Annu Rev Neurosci       Date:  2013-07-08       Impact factor: 12.449

6.  'Artiphysiology' reveals V4-like shape tuning in a deep network trained for image classification.

Authors:  Dean A Pospisil; Anitha Pasupathy; Wyeth Bair
Journal:  Elife       Date:  2018-12-20       Impact factor: 8.140

7.  Cooperative nonlinearities in auditory cortical neurons.

Authors:  Craig A Atencio; Tatyana O Sharpee; Christoph E Schreiner
Journal:  Neuron       Date:  2008-06-26       Impact factor: 17.173

8.  Deep Learning Models of the Retinal Response to Natural Scenes.

Authors:  Lane T McIntosh; Niru Maheswaranathan; Aran Nayebi; Surya Ganguli; Stephen A Baccus
Journal:  Adv Neural Inf Process Syst       Date:  2016

9.  Peri-saccadic natural vision.

Authors:  Michael Dorr; Peter J Bex
Journal:  J Neurosci       Date:  2013-01-16       Impact factor: 6.167

10.  Reliability of cortical activity during natural stimulation.

Authors:  Uri Hasson; Rafael Malach; David J Heeger
Journal:  Trends Cogn Sci       Date:  2009-12-11       Impact factor: 20.229

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