Eva Dyer

Affiliations: 
2017- Biomedical Engineering Georgia Institute of Technology, Atlanta, GA 
Area:
Computational neuroscience
Website:
Dyerlab.gatech.edu
Google:
"Eva Dyer"
Mean distance: 14.91 (cluster 29)
 
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Publications

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Shadi K, Dyer E, Dovrolis C. (2020) Multisensory integration in the mouse cortical connectome using a network diffusion model. Network Neuroscience (Cambridge, Mass.). 4: 1030-1054
Prasad JA, Balwani AH, Johnson EC, et al. (2020) A three-dimensional thalamocortical dataset for characterizing brain heterogeneity. Scientific Data. 7: 358
Rolnick D, Dyer EL. (2019) Generative models and abstractions for large-scale neuroanatomy datasets. Current Opinion in Neurobiology. 55: 112-120
Tondravi M, Scullin W, Du M, et al. (2018) A Pipeline for Distributed Segmentation of Teravoxel Tomography Datasets. Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada. 24: 164-165
LaGrow TJ, Moore MG, Prasad JA, et al. (2018) Approximating Cellular Densities from High-Resolution Neuroanatomical Imaging Data. Conference Proceedings : ... Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual Conference. 2018: 1-4
Pandarinath C, Ames KC, Russo AA, et al. (2018) Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 38: 9390-9401
Lee TJ, Kumar A, Balwani AH, et al. (2018) Large-scale neuroanatomy using LASSO: Loop-based Automated Serial Sectioning Operation. Plos One. 13: e0206172
Yang X, De Andrade V, Scullin W, et al. (2018) Low-dose x-ray tomography through a deep convolutional neural network. Scientific Reports. 8: 2575
Du M, Vescovi R, Chard R, et al. (2018) An automated pipeline for the collection, transfer, and processing of large-scale tomography data Brain
Dyer EL, Gheshlaghi Azar M, Perich MG, et al. (2017) A cryptography-based approach for movement decoding. Nature Biomedical Engineering. 1: 967-976
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