Brain
This paper introduces a dataset capturing brain signals generated by the recognition of 100 Malayalam words, accompanied by their English translations. The dataset encompasses recordings acquired from both vocal and sub-vocal modalities for the Malayalam vocabulary. For the English equivalents, solely vocal signals were collected. This dataset is created to help Malayalam speaking patients with neuro-degenerative diseases.
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This dataset contains the trained model that accompanies the publication of the same name:
Anup Tuladhar*, Serena Schimert*, Deepthi Rajashekar, Helge C. Kniep, Jens Fiehler, Nils D. Forkert, "Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 94871-94879, 2020, doi:10.1109/ACCESS.2020.2995632. *: Co-first authors
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A quantitative understanding of how sensory signals are transformed into motor outputs places useful constraints on brain function and helps reveal the brain's underlying computations. Here we present over 8,000 animal hours of behavior recordings to investigate the nematode C. elegans' response to time-varying mechanosensory signals. We use a high-throughput optogenetic assay, video microscopy and automated behavior quantification.
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Advances in optical neuroimaging techniques now allow neural activity to be recorded with cellular resolution in awake and behaving animals. Brain motion in these recordings pose a unique challenge. The location of individual neurons must be tracked in 3D over time to accurately extract single neuron activity traces. Recordings from small invertebrates like C. elegans are especially challenging because they undergo very large brain motion and deformation during animal movement.
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