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12 results for “animal vision”
Red vision in animals is broadly associated with lighting environment but not types of visual task
<p>Red sensitivity is the exception rather than the norm in most animal groups. Among species with a long wavelength sensitive (LWS) photoreceptor, peak wavelength sensitivity (λ<sub>max</sub>) varies substantially and it is unclear whether this variation can be explained by visual tuning to the light environment or to visual tasks such as signalling or foraging. Here, we examine long wavelength sensitivity across a broad range of taxa showing diversity in LWS photoreceptor λ<sub>max</sub>: insects, crustaceans, arachnids, amphibians, reptiles, fish, sharks and rays. We identified 161 species with a LWS photoreceptor (λ<sub>max</sub> ≥ 550 nm). We found evidence supporting visual tuning to the light environment: terrestrial species had longer λ<sub>max</sub> than aquatic species, and of these, species from turbid shallow waters had longer λmax than those from clear or deep waters. Of the terrestrial species, diurnal species had longer λ<sub>max</sub> than nocturnal species, but we did not detect any differences across terrestrial habitats (closed, intermediate or open). We found no association with proxies for visual tasks such as having red morphological features or utilising flowers or coral reefs. These results support the emerging consensus that, in general, visual systems are adapted to broad range of tasks, rather than tuned to certain tasks.</p>
Red vision in animals is broadly associated with lighting environment but not types of visual task
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Phototransduction in a marine sponge provides insights into the origin of animal vision: CEL-Seq2 analyses
<p>Most cellular organisms respond to light. In animals, the phototransduction system has evolved from localised light-detecting photoreceptors to ultimately complex eyes and vision. Here, we investigate the origin of metazoan photodetection and transduction by comparing well-characterised opsin-based photosystems in neural animals with those in the sponge <em>Amphimedon queenslandica</em>. Although sponges lack neurons and opsin genes, they can respond rapidly to light. In <em>Amphimedon </em>larva, this is guided by the light-sensing posterior pigment ring. We first use cell type-specific transcriptomes to reveal that genes that characterise eumetazoan Gt- and Go-mediated photosystems are enriched in the <em>Amphimedon </em>pigment ring. We then apply a suite of cell signalling pathway agonists and antagonists to swimming larvae exposed to directional light. These experiments implicate metabotropic glutamate receptors, phospholipase-C, protein kinase C and voltage-gated calcium channel in larval phototaxis. We find that U-73122-inhibition of phospholipase-C, a key transducer of the Gq-mediated pathway, completely reverses phototactic behaviour. Together, these results are consistent with aneural sponges sharing with neural metazoans an ancestral set of signal transduction pathways that have been maintained in these disparate photosensory systems since the last common animal ancestor.</p>
Phototransduction in a marine sponge provides insights into the origin of animal vision: CEL-Seq2 analyses
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Data from: A snapping shrimp has the fastest vision of any aquatic animal
<p>Animals use their sensory systems to sample information from their environments. The physiological properties of sensory systems differ, leading animals to perceive their environments in different ways. For example, eyes have different temporal sampling rates, with faster-sampling eyes able to resolve faster-moving scenes. Eyes can also have different dynamic ranges. For every eye, there is a light level below which vision is unreliable because of an insufficient signal-to-noise ratio and a light level above which the photoreceptors are saturated. Here, we report the eyes of the snapping shrimp <i>Alpheus heterochaelis</i> have a temporal sampling rate of at least 160 Hz, making them the fastest-sampling eyes ever described in an aquatic animal. Fast-sampling eyes help flying animals detect objects moving across their retinas at high angular velocities. <i>A. heterochaelis </i>are fast-moving animals that live in turbid, structurally-complex oyster reefs and their fast-sampling eyes, like those of flying animals, may help them detect objects moving rapidly across their retinas. We also report the eyes of <i>A. heterochaelis </i>have a broad dynamic range that spans conditions from late twilight (~1 lux) to direct sunlight (~100,000 lux), a finding consistent with the circatidal activity patterns of this shallow-dwelling species.</p>
WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models
<p>#############</p> <h1>WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models</h1> <p>#############</p> <p>Authors: Valentin Gabeff, Marc Russwurm, Devis Tuia & Alexander Mathis</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the article: <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6">https://link.springer.com/article/10.1007/s11263-024-02026-6</a></p> <p>--------------------------------</p> <p>WildCLIP is a fine-tuned CLIP model that allows to retrieve camera-trap events with natural language from the Snapshot Serengeti dataset. This project intends to demonstrate how vision-language models may assist the annotation process of camera-trap datasets.</p> <p>Here we provide the processed Snapshot Serengeti data used to train and evaluate WildCLIP, along with two versions of WildCLIP (model weights).</p> <p>Details on how to run these models can be found in the project <a href="https://github.com/amathislab/wildclip">github repository</a>.</p> <h2>Provided data (images and attribute annotations): </h2> <p>The data consists of 380 x 380 image crops corresponding to the MegaDetector output of Snapshot Serengeti with a confidence threshold above 0.7. We considered only camera trap images containing single individuals.</p> <p>A description of the original data can be found on LILA <a href="https://lila.science/datasets/snapshot-serengeti">here</a>, released under the <a href="https://cdla.dev/permissive-1-0/" rel="nofollow">Community Data License Agreement (permissive variant)</a>.</p> <p>We warmly thank the authors of LILA for making the MegaDetector outputs publicly available, as well as for structuring the dataset and facilitating its access.</p> <h2>Adapted CLIP model (model weights): </h2> <p>WildCLIP models provided:</p> <ul> <li><strong>[New] WildCLIP_vitb16_t1.pth: </strong>CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Trained on both base and novel vocabulary (see paper for details).</li> <li><strong>[New] WildCLIP_vitb16_t1_lwf.pth: </strong>CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1, and with the additional VR-LwF loss. Trained on both base and novel vocabulary (see paper for details).</li> <li><strong>WildCLIP_vitb16_t1_base.pth:</strong> CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Model used for evaluation and trained on base vocabulary only. (previously named <em>WildCLIP_vitb16_t1.pth</em>)</li> <li><strong>WildCLIP_vitb16_t1t7_lwf_base.pth</strong>: CLIP model with the ViT-B/16 visual backbone trained on data with captions following templates 1 to 7, and with the additional VR-LwF loss. Model used for evaluation and trained on base vocabulary only. (previously named <em>WildCLIP_vitb16_t1t7_lwf.pth</em>)</li> </ul> <p>We also provide the CSV files containing the train / val / test splits. The train / test splits follow camera split from LILA (https://lila.science/datasets/snapshot-serengeti). The validation split is custom, and also at the camera level.</p> <ul> <li><strong>train_dataset_crops_single_animal_template_captions_T1T7_ID.csv</strong>: Train set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li><strong>val_dataset_crops_single_animal_template_captions_T1T7_ID.csv</strong>: Validation set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li><strong>test_dataset_crops_single_animal_template_captions_T1T8T10.csv</strong>: Test set with captions from templates 1, 8, 9 and 10 (columns "all captions")</li> </ul> <p>Details on how the models were trained can be found in the associated <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6" target="_blank" rel="noopener">publication</a>.</p> <h2>References: </h2> <p>If you find our code, or weights, please cite:</p> <pre>@article{gabeff2024wildclip, title={WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models}, author={Gabeff, Valentin and Ru{\ss}wurm, Marc and Tuia, Devis and Mathis, Alexander}, journal={International Journal of Computer Vision}, pages={1--17}, year={2024}, publisher={Springer} }</pre> <p>If you use the adapted Snapshot Serengeti data please also cite their article:</p> <pre>@article{swanson2015snapshot, title={Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna}, author={Swanson, Alexandra and Kosmala, Margaret and Lintott, Chris and Simpson, Robert and Smith, Arfon and Packer, Craig}, journal={Scientific data}, volume={2}, number={1}, pages={1--14}, year={2015}, publisher={Nature Publishing Group} }</pre>
Data from: A snapping shrimp has the fastest vision of any aquatic animal
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Data from: Image Calibration and Analysis Toolbox – a free software suite for measuring reflectance, colour, and pattern objectively and to animal vision
1. Quantitative measurements of colour, pattern, and morphology are vital to a growing range of disciplines. Digital cameras are readily available and already widely used for making these measurements, having numerous advantages over other techniques, such as spectrometry. However, off-the-shelf consumer cameras are designed to produce images for human viewing, meaning that their uncalibrated photographs cannot be used for making reliable, quantitative measurements. Many studies still fail to appreciate this, and of those scientists who are aware of such issues, many are hindered by a lack usable tools for making objective measurements from photographs. 2. We have developed an image processing toolbox that generates images that are linear with respect to radiance from the RAW files of numerous camera brands, and can combine image channels from multispectral cameras, including additional ultraviolet photographs. Images are then normalised using one or more grey standards to control for lighting conditions. This enables objective measures of reflectance and colour using a wide range of consumer cameras. Furthermore, if the camera's spectral sensitivities are known, the software can convert images to correspond to the visual system (cone-catch values) of a wide range of animals, enabling human and non-human visual systems to be modelled. The toolbox also provides image analysis tools that can extract luminance (lightness), colour, and pattern information. Furthermore, all processing is performed on 32-bit floating point images rather than commonly used 8-bit images. This increases precision and reduces the likelihood of data loss through rounding error or saturation of pixels, while also facilitating the measurement of objects with shiny or fluorescent properties. 3. All cameras tested using this software were found to demonstrate a linear response within each image and across a range of exposure times. Cone-catch mapping functions were highly robust, converting images to several animal visual systems and yielding data that agreed closely with spectrometer-based estimates. 4. Our imaging toolbox is freely available as an addition to the open source ImageJ software. We believe that it will considerably enhance the appropriate use of digital cameras across multiple areas of biology, in particular researchers aiming to quantify animal and plant visual signals.
Data from: An Ishihara-style test of animal colour vision
Colour vision mediates ecologically relevant tasks for many animals, such as mate choice, foraging and predator avoidance. However, our understanding of animal colour perception is largely derived from human psychophysics, even though animal visual systems differ from our own. Behavioural tests of non-human animals are required to understand how colour signals are perceived by them. Here we introduce a novel test of colour vision in animals inspired by the Ishihara colour charts, which are widely used to identify human colour deficiencies. These charts consist of dots that vary in colour, brightness and size, and are designed so that a numeral or letter is distinguishable from distractor dots for humans with normal colour vision. In our method, distractor dots have a fixed chromaticity (hue and saturation) but vary in luminance. Animals can be trained to find single target dots that differ from distractor dots in chromaticity. We provide Matlab code for creating these stimuli, which can be modified for use with different animals. We demonstrate the success of this method with triggerfish, Rhinecanthus aculeatus, and highlight behavioural parameters that can be measured, including success of finding the target dot, time to detect dot and error rate. Triggerfish quickly learnt to select target dots that differed from distractors dots regardless of the particular hue or saturation, and proved to use acute colour vision. We measured discrimination thresholds by testing the detection of target colours that were of increasing colour distances (∆S) from distractor dots in different directions of colour space. At least for some colours, thresholds indicated better discrimination than expected from the Receptor Noise Limited (RNL) model assuming 5% Weber fraction for the long-wavelength cone. This methodology seems to be highly effective because it resembles natural foraging behavior for the triggerfish and may well be adaptable to a range of other animals, including mammals, birds, bees and freshwater fish. Other questions may be addressed using this methodology, including luminance thresholds, sensory bias, effects of sensory noise in detection tasks, colour categorization and saliency
Data from: A computer vision for animal ecology
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Data from: An Ishihara-style test of animal colour vision
Open the record for dataset details and reuse information.
Data from: Image Calibration and Analysis Toolbox – a free software suite for measuring reflectance, colour, and pattern objectively and to animal vision
Open the record for dataset details and reuse information.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.