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All Categories (1-14 of 14)

  1. Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 2

    18 Sep 2017 | Datasets | Contributor(s):

    By Haiguang Wen1, Junxing Shi1, Yizhen Zhang1, Kun-Han Lu1, Jiayue Cao, Zhongming Liu2

    1. Purdue University 2. Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, Purdue University

    This is a video-fMRI dataset for subject 2 (out of three) acquired by the Laboratory of Integrated Brain Imaging (LIBI).

    https://purr.purdue.edu/publications/2806/?v=1

  2. Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests

    15 Sep 2017 | Series/Dataset | Contributor(s):

    By Haiguang Wen1, Junxing Shi1, Yizhen Zhang1, Kun-Han Lu1, Jiayue Cao, Zhongming Liu2

    1. Purdue University 2. Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, Purdue University

    This is a video-fMRI dataset contains the videos with stimuli acquired by the Laboratory of Integrated Brain Imaging (LIBI).

    https://purr.purdue.edu/publications/2809/?v=1

  3. Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 3

    15 Sep 2017 | Datasets | Contributor(s):

    By Haiguang Wen1, Junxing Shi1, Yizhen Zhang1, Kun-Han Lu1, Jiayue Cao, Zhongming Liu2

    1. Purdue University 2. Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, Purdue University

    This is a video-fMRI dataset for subject 3 (out of three) acquired by the Laboratory of Integrated Brain Imaging (LIBI).

    https://purr.purdue.edu/publications/2807/?v=1

  4. Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 1

    15 Sep 2017 | Datasets | Contributor(s):

    By Haiguang Wen1, Junxing Shi1, Yizhen Zhang1, Kun-Han Lu1, Jiayue Cao, Zhongming Liu2

    1. Purdue University 2. Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, Purdue University

    This is a video-fMRI dataset for subject 1 (out of three) acquired by the Laboratory of Integrated Brain Imaging (LIBI),

    https://purr.purdue.edu/publications/2805/?v=1

  5. Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Stimuli

    15 Sep 2017 | Datasets | Contributor(s):

    By Haiguang Wen1, Junxing Shi1, Yizhen Zhang1, Kun-Han Lu1, Jiayue Cao, Zhongming Liu2

    1. Purdue University 2. Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, Purdue University

    This is a video-fMRI dataset contains the videos with stimuli acquired by the Laboratory of Integrated Brain Imaging (LIBI).

    https://purr.purdue.edu/publications/2808/?v=1

  6. Mathematica Files: Competitive tuning: competition’s role in setting the frequency-dependence of Ca2+-dependent proteins

    02 Sep 2017 | Datasets | Contributor(s):

    By Daniel Romano1, Matthew C Pharris1, Neal Patel1, Tamara Kinzer-Ursem1

    Purdue University

    We study the competition among seven well-studied neuronal proteins for their common binding partner, calmodulin. We find that competition narrows and shifts the range over which proteins can be...

    https://purr.purdue.edu/publications/2789/?v=1

  7. Visualized layer-wise visual features in deep residual neural network

    29 Jun 2017 | Datasets | Contributor(s):

    By Haiguang Wen1, Junxing Shi1, Wei Chen2, Zhongming Liu3

    1. Purdue University 2. Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA 3. Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, Purdue University

    Deep residual neural network is a brain-inspired computational model. 50 layers of neuron-like computational units are stacked into a bottom-up hierarchy. Features encoded at units are visualized...

    https://purr.purdue.edu/publications/2590/?v=1

  8. Drosophila Optical Stimulator

    27 Jun 2017 | Datasets | Contributor(s):

    By Xinping Chen1, Walter Leon-Salas1, Taylor Zigon1, Donald Ready1, Vikki Weake1

    Purdue University

    This publication contains the electronic files required to build an optical stimulator for fruit flies. The stimulator uses red and blue light-emitting diodes (LEDs) and an embedded computer to...

    https://purr.purdue.edu/publications/2598/?v=1

  9. Drosophila Optical Stimulator - software files

    15 Jun 2017 | Datasets | Contributor(s):

    By Xinping Chen1, Walter Leon-Salas1, Taylor Zigon1, Donald Ready1, Vikki Weake1

    Purdue University

    This publication contains the software required to build an optical stimulator for fruit flies. The stimulator uses red and blue light-emitting diodes (LEDs) and an embedded computer to generate...

    https://purr.purdue.edu/publications/2591/?v=1

  10. Drosophila Optical Stimulator - hardware files

    15 Jun 2017 | Datasets | Contributor(s):

    By Xinping Chen1, Walter Leon-Salas1, Taylor Zigon1, Donald Ready1, Vikki Weake1

    Purdue University

    This publication contains the electronic files required to build an optical stimulator for fruit flies. The stimulator uses red and blue light-emitting diodes (LEDs) and an embedded computer to...

    https://purr.purdue.edu/publications/2573/?v=1

  11. Simplicity of K-means versus deepness of Deep Learning. A Case of Unsupervised Feature Learning with Limited Data

    30 Sep 2015 | Datasets | Contributor(s):

    By Murat Dundar1, Qiang Kou2, Baichuan Zhang1, Yicheng He1, Bartlomiej P. Rajwa3

    1. Department of Computer and Information Sciences, IUPUI 2. Department of Biohealth Informatics, IUPUI 3. Bindley Bioscience Center, Purdue University

    A study contrasting K-means-based unsupervised feature learning and deep learning techniques for small data sets with limited intra- as well as inter-class diversity

    https://purr.purdue.edu/publications/1988/?v=1

  12. Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signal

    28 Sep 2015 | Datasets | Contributor(s):

    By Haiguang Wen1, Zhongming Liu2

    1. Purdue University 2. Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, Purdue University

    Matlab toolbox for separating the fractal (scale-free) component and oscillatory component in the power spectrum from the mixed time series

    https://purr.purdue.edu/publications/1987/?v=1

  13. Signal Processing Toolbox for Simultaneously Acquired fMRI and EEG

    16 Jul 2015 | Datasets | Contributor(s):

    By Rodrigo Castellanos1, Zhongming Liu2

    1. School of Electrical and Computer Engineering, Purdue University 2. Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, Purdue University

    This Matlab toolbox includes signal processing functions to remove gradient and pulse artifacts in EEG data recorded simultaneously with fMRI. It is distributed as a GUI plugin for EEGLAB.

    https://purr.purdue.edu/publications/1936/?v=1

  14. A Non-parametric Bayesian Model for Joint Cell Clustering and Cluster Matching: Identification of Anomalous Sample Phenotypes with Random Effects.

    02 Sep 2014 | Datasets | Contributor(s):

    By Murat Dundar1, Ferit Akova1, Halid Ziya Yerebakan1, Bartlomiej P. Rajwa2

    1. Indiana University - Purdue University, Indianapolis 2. Purdue University

    The manuscript presents a non-parametric Bayesian algorithm called ASPIRE (Anomalous Sample Phenotype Identification with Random Effects) able to identify phenotypic differences across batches of...

    https://purr.purdue.edu/publications/1712/?v=1