Natural Images from the Birthplace of the Human Eye

Tkačik, Gašper; Garrigan, Patrick; Ratliff, Charles P.; Milčinski, Grega; Klein, Jennifer M.; Seyfarth, Lucia H.; Sterling, Peter; Brainard, David H.; Balasubramanian, Vijay

doi:10.1371/journal.pone.0020409

Cited by 93 publications

(114 citation statements)

References 54 publications

Supporting

Mentioning

113

Contrasting

Order By: Relevance

“…Static natural jittered scenes consisted of images drawn from a natural image database 32 and drifted in two dimensions with the approximate statistics of fixational eye movements 17 . The image also changed to a different location every one second, representing a saccade-like transition.…”

Section: Methodsmentioning

confidence: 99%

The dynamic neural code of the retina for natural scenes

Maheswaranathan

Tanaka

et al. 2018

Preprint

View full text Add to dashboard Cite

The normal function of the retina is to convey information about natural visual images. It is this visual environment that has driven evolution, and that is clinically relevant. Yet nearly all of our understanding of the neural computations, biological function, and circuit mechanisms of the retina comes in the context of artificially structured stimuli such as flashing spots, moving bars and white noise. It is fundamentally unclear how these artificial stimuli are related to circuit processes engaged under natural stimuli. A key barrier is the lack of methods for analyzing retinal responses to natural images. We addressed both these issues by applying convolutional neural network models (CNNs) to capture retinal responses to natural scenes. We find that CNN models predict natural scene responses with high accuracy, achieving performance close to the fundamental limits of predictability set by intrinsic cellular variability. Furthermore, individual internal units of the model are highly correlated with actual retinal interneuron responses that were recorded separately and never presented to the model during training. Finally, we find that models fit only to natural scenes, but not white noise, reproduce a range of phenomena previously described using distinct artificial stimuli, including frequency doubling, latency encoding, motion anticipation, fast contrast adaptation, synchronized responses to motion reversal and object motion sensitivity. Further examination of the model revealed extremely rapid context dependence of retinal feature sensitivity under natural scenes using an analysis not feasible from direct examination of retinal responses. Overall, these results show that nonlinear retinal processes engaged by artificial stimuli are also engaged in and relevant to natural visual processing, and that CNN models form a powerful and unifying tool to study how sensory circuitry produces computations in a natural context.

show abstract

Section: Methodsmentioning

confidence: 99%

The dynamic neural code of the retina for natural scenes

Maheswaranathan

Tanaka

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…• every 4-6 min [Johnsen et al, 2006], and a wide range of changes in the intensity and color of light can occur over a span of 2-4 hr [e.g., Tkačik et al, 2011]. For example, the spectral composition of twilight differs from day and night, being enriched in short wavelengths and distinctly purplish in Madagascar [Pariente, 1980] and at northern latitudes [Endler, 1993;Lee & Hernández-Andrés, 2003;Johnsen et al, 2006].…”

Section: Activity In the Twilight Zonementioning

confidence: 99%

Why Aye‐Ayes See Blue

Melin

Moritz

Fosbury

et al. 2012

American J Primatol

View full text Add to dashboard Cite

The capacity for cone-mediated color vision varies among nocturnal primates. Some species are colorblind, having lost the functionality of their short-wavelength-sensitive-1 (SWS1) opsin pigment gene. In other species, such as the aye-aye (Daubentonia madagascariensis), the SWS1 gene remains intact. Recent studies focused on aye-ayes indicate that this gene has been maintained by natural selection and that the pigment has a peak sensitivity (lambda(max)) of 406 nm, which is -20 nm closer to the ultraviolet region of the spectrum than in most primates. The functional significance behind the retention and unusual lambda(max) of this opsin pigment is unknown, and it is perplexing given that all mammals are presumed to be colorblind in the dark. Here we comment on this puzzle and discuss recent findings on the color vision intensity thresholds of terrestrial vertebrates with comparable optics to aye-ayes. We draw attention to the twilight activities of aye-ayes and report that twilight is enriched in short-wavelength (bluish) light. We also show that the intensity of twilight and full moonlight is probably sufficient to support cone-mediated color vision. We speculate that the intact SWS1 opsin pigment gene of aye-ayes is a crepuscular adaptation and we report on the blueness of potential visual targets, such as scent marks and the brilliant blue arils of Ravenala madagascariensis.

show abstract

“…We first downloaded 2897 different images without human faces from [4,9,5], National Oceanic and Atmospheric Administration (NOAA) Photo Library 2 and European Southern Observatory 3 . They were divided into two sets containing 2451 and 446 images respectively for training and validation.…”

Section: Dataset and Experimentsmentioning

confidence: 99%

An Analysis of the Viola-Jones Face Detection Algorithm

Wang¹

2014

Image Processing on Line

279

109

View full text Add to dashboard Cite

In this article, we decipher the Viola-Jones algorithm, the first ever real-time face detection system. There are three ingredients working in concert to enable a fast and accurate detection: the integral image for feature computation, Adaboost for feature selection and an attentional cascade for efficient computational resource allocation. Here we propose a complete algorithmic description, a learning code and a learned face detector that can be applied to any color image. Since the Viola-Jones algorithm typically gives multiple detections, a post-processing step is also proposed to reduce detection redundancy using a robustness argument. Source CodeThe source code and the online demo are accessible at the IPOL web page of this article 1 .

show abstract

Natural Images from the Birthplace of the Human Eye

Cited by 93 publications

References 54 publications

The dynamic neural code of the retina for natural scenes

The dynamic neural code of the retina for natural scenes

Why Aye‐Ayes See Blue

An Analysis of the Viola-Jones Face Detection Algorithm

Contact Info

Product

Resources

About