COCO - Common Objects in Context
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相关论文
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2026
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0Recent advancements in deep neural networks (DNNs), particularly large-scale language models, have demonstrated remarkable capabilities in image and natural language understanding. Although scaling up model parameters with increasing volume of training data has progressively improved DNN capabilitie...
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Cerebro-cerebellar networks facilitate learning through feedback decoupling
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Nature Communications
2023
•2023/1/4
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Behavioural feedback is critical for learning in the cerebral cortex. However, such feedback is often not readily available. How the cerebral cortex learns efficiently despite the sparse nature of feedback remains unclear. Inspired by recent deep learning algorithms, we introduce a systems-level com...
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Re-expression of CA1 and entorhinal activity patterns preserves temporal context memory at long timescales
Futing Zou•Guo Wanjia•Emily J. Allen•Yihan Wu•Ian Charest等 10 人
Nature Communications
2023
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Converging, cross-species evidence indicates that memory for time is supported by hippocampal area CA1 and entorhinal cortex. However, limited evidence characterizes how these regions preserve temporal memories over long timescales (e.g., months). At long timescales, memoranda may be encountered in ...
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10.1038/S41467-023-40100-8
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Npj Artificial Intelligence
2025
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Addressing the open-environment issue with pure data-driven approaches, especially for large models (LM) that require great efforts for data curation and mix, training recipes, and collaboration with small models, makes current Automated machine learning (AutoML) systems inefficient and computationa...
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Allen Emily J.•St-Yves Ghislain•Wu Yihan•Breedlove Jesse L.•Prince Jacob S.等 13 人
Nature Neuroscience
2021
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Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in which high-resolution functional magnetic resonance imaging responses to tens of thousands of richly annotated natural sc...
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Nature Communications
2026
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Attention is a cornerstone of cognition and neural computation, enabling the brain to select relevant information, bind features into coherent objects, and guide behavior. However, we currently lack a unifying computational model that connects the diverse phenomena of attention, from spatial and fea...
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Liangwei Fan•Hui Shen•Xiangkai Lian•Yulin Li•Man Yao等 7 人
Nature Communications
2025
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Spiking neural networks (SNNs) are biologically more plausible and computationally more powerful than artificial neural networks due to their intrinsic temporal dynamics. However, vanilla spiking neurons struggle to simultaneously encode spatiotemporal dynamics of inputs. Inspired by biological mult...
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Nature Machine Intelligence
2023
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Existing applications of deep learning in computational imaging and microscopy mostly depend on supervised learning, requiring large-scale, diverse and labelled training data. The acquisition and preparation of such training image datasets is often laborious and costly, leading to limited generaliza...
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Roth Zvi N.•Kay Kendrick•Merriam Elisha P.
Nature Communications
2022
•2022/10/29
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Orientation selectivity in primate visual cortex is organized into cortical columns. Since cortical columns are at a finer spatial scale than the sampling resolution of standard BOLD fMRI measurements, analysis approaches have been proposed to peer past these spatial resolution limitations. It was r...
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Concept whitening for interpretable image recognition
Zhi Chen•Yijie Bei•Cynthia Rudin
Nature Machine Intelligence
2020
•2020/12/7
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What does a neural network encode about a concept as we traverse through the layers? Interpretability in machine learning is undoubtedly important, but the calculations of neural networks are very challenging to understand. Attempts to see inside their hidden layers can be misleading, unusable or re...
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Representations in human primary visual cortex drift over time
Zvi N. Roth•Elisha P. Merriam
Nature Communications
2023
•2023/7/21
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Primary sensory regions are believed to instantiate stable neural representations, yet a number of recent rodent studies suggest instead that representations drift over time. To test whether sensory representations are stable in human visual cortex, we analyzed a large longitudinal dataset of fMRI r...
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10.1038/S41467-023-40144-W
ISSN:2041-1723