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10 results for “object representation”
Dataset for publication: 'Search Space Representation and Reduction Methods to Enhance Multi-Objective Water Supply Monitoring Design'
<p>This is the dataset the publication "Search Space Representation and Reduction Methods to Enhance Multi-Objective Water Supply Monitoring Design" is based on.</p>
Data from: Neural signatures for sustaining object representations attributed to others in preverbal human infants
A major feat of social beings is to encode what their conspecifics see, know or believe. While various nonhuman animals show precursors of these abilities, humans perform uniquely sophisticated inferences about other people's mental states. However, it is still unclear how these possibly human-specific capacities develop and whether preverbal infants, similarly to adults form representations of other agents' mental states, specifically metarepresentations. We explored the neuro-cognitive bases of 8-month-olds' ability to encode the world from another person's perspective, using gamma-band EEG activity over the temporal lobes, an established neural signature for sustained object representation after occlusion. We observed such gamma-band activity when an object was occluded from the infants' perspective, as well as when it was occluded only from the other person (Experiment 1), and also when subsequently the object disappeared but the person falsely believed the object to be present (Experiment 2). These findings suggest that the cognitive systems involved in representing the world from infants' own perspective are also recruited for encoding others' beliefs. Such results point to an early developing, powerful apparatus suitable to deal with multiple concurrent representations; and suggest that infants can have a metarepresentational understanding of other minds even before the onset of language.
Figure 1 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 1 Conceptual model of a Knowledge Object (KO) containing a payload, machine-actionable service and deployment specifications, metadata and a unique persistent identifier. We are exploring aligning our conceptual model with emerging best practices for FAIR Digital Objects. Derived from Wittenburg et al's Digital Objects as Drivers towards Convergence in Data Infrastructures (Wittenburg et al. 2019).
Figure 3 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 3 This figure illustrates the dual nature of Knowledge Objects: knowledge-as-resource and knowledge-as-service. A KO can be curated and maintained in a repository, pass metadata to a knowledge graph or deployed into applications. Different to other digital objects, the methods to deploy the KO to applications via custom or generic runtimes called by microservices are built into the KO.
Figure 2 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 2 (L) Sample KO as viewed from the KGrid Library, from which the KO can be implemented in a hosted runtime environment or downloaded. (R) Sample output results from deploying the KO.
Object color knowledge representation occurs in the macaque brain despite the absence of a developed language system
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Data from: Emerging Representational Geometries in the Visual System Predict Reaction Times for Object Categorization
Recognizing an object takes just a fraction of a second, less than the blink of an eye. Applying multivariate pattern analysis, or "brain decoding", methods to magnetoencephalography (MEG) data has allowed researchers to characterize, in high temporal resolution, the emerging representation of objects that underlie our capacity for rapid recognition. Shortly after stimulus onset, exemplar stimuli cluster by category in high-dimensional activation spaces. In these emerging activation spaces, the decodability of exemplar category varies over time, reflecting the brain's transformation of visual inputs into coherent categorical representations. How do these emerging representations relate to categorization behavior? Recently it has been proposed that the distance of an exemplar representation from a categorical boundary in an activation space is critical for perceptual decision-making, and that reaction times should therefore correlate with distance from the boundary. The predictions of this distance hypothesis have been born out in human inferior temporal cortex (IT), an area of the brain crucial for the representation of object categories. The time of peak decoding is the optimal time for category information to be "read out" from the brain's time varying representation of the stimuli. In this study, we tested the distance hypothesis, and specifically whether or not the brain reads out at the optimal time for choice behavior. Using MEG decoding methods, we show that the distance of a pattern of activity from a decision boundary through a high-dimensional activation space correlates with reaction times in a visual categorization task, but only during the period of peak decodability. Our results suggest the brain uses the optimal stimulus representation for choice behavior, and that neural representations for objects are partially constitutive of the decision process in visual perception.
Data from: Emerging Representational Geometries in the Visual System Predict Reaction Times for Object Categorization
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Data from: Neural signatures for sustaining object representations attributed to others in preverbal human infants
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Data from: Emergence of transformation-tolerant representations of visual objects in rat lateral extrastriate cortex
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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.