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2,721 results for “Connectivity”
Figure 2 in The connection of the intensity of the plankton community luminescence and the age distribution of horse mackerel in the coastal waters of the south-western Crimea
Figure 2. The average monthly intensity of glow organisms 1 – in the winter (January-February); 2 – in the spring (May); 3 — in the summer (June-July) in the 2010-2015.
Figure 3 in The connection of the intensity of the plankton community luminescence and the age distribution of horse mackerel in the coastal waters of the south-western Crimea
Figure 3. Relative quantity of the age groups the Trachurus mediterraneus (1-yearlings. 2 - two-year-olds. 3 - threeyear-olds. 4 - four-year-olds. 5 - five-year-olds) in the spring-summer period in the coastal waters of the south-western Crimea.
Fig. 7. Connection between UABM and ulnerve nerve. A in The Nerve Bundle via the Median Nerve Innervating the Ulnar Intrinsic Muscles of the Hand in a Gorilla Equivalent to the Deep Branch of the Ulnar Nerve in the Human
Fig. 7. Connection between UABM and ulnerve nerve. A. In the proximal forearm, a thin branch from the ulnar nerve meets a branch from the UABM to make a neural arch that gives off motor branches to the flexor digitorum profundus (FDP) of the ring and little fingers. B. A thin proximal branch (hollow arrow) from the ulnar nerve unites with the radial bundle (motor fasciculi) of the UABM and the other thicker distal one (solid arrow) unites with its ulnar bundle (sensory fasciculi).
Fig. 3 in Fig. 3 in Genetic Structure of the Mangrove Killifish Costa, 2011 (Cyprinodontiformes: Aplocheiloidei) Supports A Wide Connection among its Populations.
Fig. 3. Haplotype network of the Kryptolebias marmoratus species group. Maps represent the distribution of each group.
Fig. 2 in Fig. 3 in Genetic Structure of the Mangrove Killifish Costa, 2011 (Cyprinodontiformes: Aplocheiloidei) Supports A Wide Connection among its Populations.
Fig. 2. Distribution of K. hermaphroditus: Orange star indicates type locality; and Green circles indicate recorded localities for the species (Costa 2011; 2016; Sarmento-Soares et al. 2014; Lira et al. 2015; Berbel-Filho et al. 2016; Guimarães-Costa et al. 2017; Tatarenkov et al. 2017a; This study).
Fig. 1 in Fig. 3 in Genetic Structure of the Mangrove Killifish Costa, 2011 (Cyprinodontiformes: Aplocheiloidei) Supports A Wide Connection among its Populations.
Fig. 1. Kryptolebias hermaphroditus from Tutóia, Maranhão State, Delta do Parnaíba, north eastern Brazil; UFRJ12666: A: Hermaphrodite, 35.5 mm SL; B: Male, 20.3 mm SL; C: Male, 28.9 mm SL.
Fig. 7 in A new cyclopoid copepod from Korean subterranean waters reveals an interesting connection with the Central Asian fauna (Crustacea: Copepoda: Cyclopoida)
Fig. 7. Monchenkocyclops changi gen. et sp. nov., allotype male. A. habitus, dorsal view. B. urosome, ventral view. C. right caudal ramus, dorsal view. D. right caudal ramus, lateral view. E. second endopodal segment of fourth swimming leg, anterior view. F. sixth leg, ventrolateral view. Arabic numerals indicating sensilla and pores consecutively from anterior to posterior end of body, and from dorsal to ventral side (excluding appendages). Scale bars 100 µm.
Fig. 1 in A new cyclopoid copepod from Korean subterranean waters reveals an interesting connection with the Central Asian fauna (Crustacea: Copepoda: Cyclopoida)
Fig. 1. Monchenkocyclops changi gen. et sp. nov., holotype female: A. habitus, dorsal view. B. antennula, dorsal view. Arabic numerals indicating sensilla and pores consecutively from anterior to posterior end of body, and from dorsal to ventral side (excluding appendages; those on cephalothorax not presented). Scale bars 100 µm.
Fig. 6 in A new cyclopoid copepod from Korean subterranean waters reveals an interesting connection with the Central Asian fauna (Crustacea: Copepoda: Cyclopoida)
Fig. 6. Monchenkocyclops changi gen. et sp. nov., A-E. holotype female. F. allotype male. A. second endopodal segment of third swimming leg, anterior view. B. left fourth swimming leg, anterior view. C. second endopodal segment of right fourth swimming leg, anterior view. D. fifth leg, anterior view. E. sixth leg, lateral view. Scale bar 100 µm.
Fig. 4 in A new cyclopoid copepod from Korean subterranean waters reveals an interesting connection with the Central Asian fauna (Crustacea: Copepoda: Cyclopoida)
Fig. 4. Monchenkocyclops changi gen. et sp. nov., A-E. holotype female. F. paratype female. A. urosome, dorsal view. B. antenna, dorsal view. C. labrum, anterior view. D. maxillula, posterior view. E. mandibula, anterior view. F. cutting edge of labrum, anterior view. Arabic numerals indicating sensilla and pores consecutively from anterior to posterior end of body, and from dorsal to ventral side (excluding appendages). Scale bars 100 µm.
Fig. 5 in A new cyclopoid copepod from Korean subterranean waters reveals an interesting connection with the Central Asian fauna (Crustacea: Copepoda: Cyclopoida)
Fig. 5. Monchenkocyclops changi gen. et sp. nov., holotype female: A. maxilla, anterior view. B. maxilliped, posterior view. C. first swimming leg, anterior view. D. second swimming leg, anterior view. Scale bar 100 µm.
Fig. 3 in A new cyclopoid copepod from Korean subterranean waters reveals an interesting connection with the Central Asian fauna (Crustacea: Copepoda: Cyclopoida)
Fig. 3. Monchenkocyclops changi gen. et sp. nov., holotype female: A. urosome, ventral view. B. urosome, lateral view. Arabic numerals indicating sensilla and pores consecutively from anterior to posterior end of body, and from dorsal to ventral side (excluding appendages). Scale bars 100 µm.
Fig. 2 in A new cyclopoid copepod from Korean subterranean waters reveals an interesting connection with the Central Asian fauna (Crustacea: Copepoda: Cyclopoida)
Fig. 2. Monchenkocyclops changi gen. et sp. nov., holotype female: A. cephalothoracic shield, lateral view. B. cephalothorax, dorsal view. C. pleurons of free prosomites, lateral view. D. rostrum, dissected and flattened, original anterior view. E. pleuron of second free prosomite (third pedigerous somite), dissected and flattened. Arabic numerals indicating sensilla and pores consecutively from anterior to posterior end of body, and from dorsal to ventral side (excluding appendages). Scale bars 100 µm.
The dataset on structural connectivity in high-mountain Asia
<p>This is the inaugural version of the dataset, comprising 10 slices in GeoTIFF format at a resolution of 30 meters, provided for reviewers to assess our manuscript. We will release the full version of the dataset upon acceptance of the article. For any inquiries, please contact the following email address. jinlongli@stu.scu.edu.cn.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 16. Architecture for Affective Situation Assessment of Perceptual Images (Internal Connections between Emotions are not Depicted for Better Clarity of the Graphic)
<p>Based on the concept of affective neuro-symbols, a model was developed according to<br> which emotions can be represented by affective neuro-symbolic networks (see right half of Figure<br> 16, referred to as architecture of “internal perception” in contrast to the “external perception”<br> architecture of the left half of Figure 16, which has already been presented in Section 4.2).<br> The individual affective neuro-symbols (depicted as circles) represent different emotions<br> (fear, anger, guilt, joy, rage, panic, love, happiness, etc.).</p>
Figure3. APCs A1-A4 connected within range of cohesion-factor-threshold form members of one ARB-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>Figure3 depicts this process. The<br> model with the above specification then effectively detects self or non-self pathogens. In terms of<br> its application to the task of clustering, this interpretation means making the affinities high within<br> clusters and low across clusters. A pathogen corresponding to an outlier would not stimulate a TCR<br> sufficiently and may not form part of any ARB.</p>
Figure 7. Adding an Acting Module, its configuration values and its input connections-Designing a Growing Functional Modules "Artificial Brain"
<p>The fourth step consists of adding an Acting Module, its configuration values and input<br> connection as shown in figure 7. A type “CI” is assigned because it functionality will consist of<br> triggering a steering command in accordance with the perception from the Sensing Module and in<br> order to satisfy the input request from the Global Goal. Consequently, the feedback is set to “1 18”<br> where “1” is the reference to the Sensation “free” and “18” to the perception in output of the<br> Sensing Module. The identifier “18” for this perception is computed as at the total number of<br> Sensation plus one (first sensing module). Identifiers and their references are automatically updated<br> when a Sensation is added or deleted.</p>
Figure 5. Adding connections from Sensations 2-17 to the Sensing Module 1-Designing a Growing Functional Modules "Artificial Brain"
<p>The next step consists of connecting the sixteen sensations in the input of the Sensing<br> Module. To do this, the user must right-click on each Sensation, then on “new connection” and<br> indicate the Sensing Module identifier. The resulting design is presented on figure 5. Finally, the<br> Acting Module's field is set to “1” (indicating the number of the Acting Module that later will assess<br> the correctness of the perception) and the unique extra-parameter set to “20” (related with the<br> module's behavior).</p>
Figure 2. Results for firm name "Váhostav" are in table. Each row defines a firm with its name, identification number and address. Then a connection is specified (whether it be a person or another firm).-Browsing Semantic Data in Slovakia
<p>We have searched for firm “Váhostav”, which is a rather big firm in Slovakia, with many press articles published about31. On Figure 2 there is a browsing window, for SBR data results, displaying tabular structure, which was refined from SBR dataset by continuous querying.</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 4. a) Sparse Connectivity, b) Shared Weights (Convolutional Neural Networks (LeNet), 2018)
<p>A CNNs are biologically inspired variants of a multilayer perceptron. CNNs establish a built-in local correlation by applying a local link model between the neurons of adjacent layers. As shown in Figure 4a, the inputs of the hidden units in the m-layer are obtained from a subset of the units having the built-in areas in the m-1 layer. This ensures that a number of layers arrive consecutively, resulting in a filtration. It can encode 5 features such as a neuron in the m+1 hidden layer. On CNNs, each filter hi is repeated on the entire image surface. These repeated units form a feature map that shares the weight and bias parameters. 3 hidden units of the same feature map are shown in Figure 4b. The weights shown by parallel lines in the figure were also limited as same. To learn such shared parameters, the gradient method can be used with only a small modification to the original parameters. The sum of the inverse gradients of the shared parameters equals the gradient of the shared weights.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.