CEBRA
Uncover hidden structures in your data with CEBRA
CEBRA is a self-supervised learning algorithm that provides interpretable embeddings of high-dimensional recordings using auxiliary variables. It excels in compressing time series data to reveal hidden structures, particularly in behavioral and neural data. The algorithm can decode activity from the visual cortex, reconstruct viewed videos, decode trajectories, and determine position during navigation. CEBRA is a valuable tool for joint behavioral and neural analysis, offering consistent and high-performance latent spaces for hypothesis testing and label-free usage across various datasets and species.
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CEBRA
CEBRA is a self-supervised learning algorithm that provides interpretable embeddings of high-dimensional recordings using auxiliary variables. It excels in compressing time series data to reveal hidden structures, particularly in behavioral and neural data. The algorithm can decode activity from the visual cortex, reconstruct viewed videos, decode trajectories, and determine position during navigation. CEBRA is a valuable tool for joint behavioral and neural analysis, offering consistent and high-performance latent spaces for hypothesis testing and label-free usage across various datasets and species.
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CEBRA
CEBRA is a self-supervised learning algorithm that provides interpretable embeddings of high-dimensional recordings using auxiliary variables. It excels in compressing time series data to reveal hidden structures, particularly in behavioral and neural data. The algorithm can decode activity from the visual cortex, reconstruct viewed videos, decode trajectories, and determine position during navigation. CEBRA is a valuable tool for joint behavioral and neural analysis, offering consistent and high-performance latent spaces for hypothesis testing and label-free usage across various datasets and species.
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Mind-Video is an AI tool that focuses on high-quality video reconstruction from brain activity data. It bridges the gap between image and video brain decoding by utilizing masked brain modeling, multimodal contrastive learning, spatiotemporal attention, and co-training with an augmented Stable Diffusion model. The tool aims to recover accurate semantic information from fMRI signals, enabling the generation of realistic videos based on brain activities.
For similar jobs
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CEBRA is a self-supervised learning algorithm that provides interpretable embeddings of high-dimensional recordings using auxiliary variables. It excels in compressing time series data to reveal hidden structures, particularly in behavioral and neural data. The algorithm can decode activity from the visual cortex, reconstruct viewed videos, decode trajectories, and determine position during navigation. CEBRA is a valuable tool for joint behavioral and neural analysis, offering consistent and high-performance latent spaces for hypothesis testing and label-free usage across various datasets and species.
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