Добірка наукової літератури з теми "Computational neuroimaging"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Computational neuroimaging".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Computational neuroimaging"
Stephan, Klaas E., Sandra Iglesias, Jakob Heinzle, and Andreea O. Diaconescu. "Translational Perspectives for Computational Neuroimaging." Neuron 87, no. 4 (August 2015): 716–32. http://dx.doi.org/10.1016/j.neuron.2015.07.008.
Повний текст джерелаPotter, Aneirin. "044 At what resolution does the brain perform computations?" Journal of Neurology, Neurosurgery & Psychiatry 93, no. 9 (August 12, 2022): e2.239. http://dx.doi.org/10.1136/jnnp-2022-abn2.88.
Повний текст джерелаWandell, Brian A. "COMPUTATIONAL NEUROIMAGING OF HUMAN VISUAL CORTEX." Annual Review of Neuroscience 22, no. 1 (March 1999): 145–73. http://dx.doi.org/10.1146/annurev.neuro.22.1.145.
Повний текст джерелаWandell, Brian A., and Jonathan Winawer. "Computational neuroimaging and population receptive fields." Trends in Cognitive Sciences 19, no. 6 (June 2015): 349–57. http://dx.doi.org/10.1016/j.tics.2015.03.009.
Повний текст джерелаFriston, Karl J., and Raymond J. Dolan. "Computational and dynamic models in neuroimaging." NeuroImage 52, no. 3 (September 2010): 752–65. http://dx.doi.org/10.1016/j.neuroimage.2009.12.068.
Повний текст джерелаStephan, K. E., F. Schlagenhauf, Q. J. M. Huys, S. Raman, E. A. Aponte, K. H. Brodersen, L. Rigoux, et al. "Computational neuroimaging strategies for single patient predictions." NeuroImage 145 (January 2017): 180–99. http://dx.doi.org/10.1016/j.neuroimage.2016.06.038.
Повний текст джерелаDi Ieva, Antonio, Mounir Boukadoum, Salim Lahmiri, and Michael D. Cusimano. "Computational Analyses of Arteriovenous Malformations in Neuroimaging." Journal of Neuroimaging 25, no. 3 (December 17, 2014): 354–60. http://dx.doi.org/10.1111/jon.12200.
Повний текст джерелаPoldrack, Russell A., Krzysztof J. Gorgolewski, and Gaël Varoquaux. "Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging." Annual Review of Biomedical Data Science 2, no. 1 (July 20, 2019): 119–38. http://dx.doi.org/10.1146/annurev-biodatasci-072018-021237.
Повний текст джерелаRitter, Petra, Michael Schirner, Anthony R. McIntosh, and Viktor K. Jirsa. "The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging." Brain Connectivity 3, no. 2 (April 2013): 121–45. http://dx.doi.org/10.1089/brain.2012.0120.
Повний текст джерелаGoldstein-Piekarski, Andrea N., Bailey Holt-Gosselin, Kathleen O’Hora, and Leanne M. Williams. "Integrating sleep, neuroimaging, and computational approaches for precision psychiatry." Neuropsychopharmacology 45, no. 1 (August 19, 2019): 192–204. http://dx.doi.org/10.1038/s41386-019-0483-8.
Повний текст джерелаДисертації з теми "Computational neuroimaging"
Macoveanu, Julian. "Neural mechanisms underlying working memory : computational and neuroimaging studies /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-901-7/.
Повний текст джерелаWhalley, Matthew G. "Autobiographical memory in depression : neuroimaging and computational linguistic investigation." Thesis, University of London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.542382.
Повний текст джерелаCattinelli, I. "INVESTIGATIONS ON COGNITIVE COMPUTATION AND COMPUTATIONAL COGNITION." Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155482.
Повний текст джерелаGradin, Iade Victoria B. "Major depression and schizophrenia : investigation of neural mechanisms using neuroimaging and computational modeling of brain function." Thesis, University of Aberdeen, 2011. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=184011.
Повний текст джерелаSalimi-Khorshidi, Gholamreza. "Statistical models for neuroimaging meta-analytic inference." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:40a10327-7f36-42e7-8120-ae04bd8be1d4.
Повний текст джерелаD'ANGELO, LAURA. "Bayesian modeling of calcium imaging data." Doctoral thesis, Università degli Studi di Padova, 2022. https://hdl.handle.net/10281/399067.
Повний текст джерелаPetitet, Pierre. "Sensorimotor adaptation : mechanisms, modulation and rehabilitation potential." Thesis, University of Oxford, 2018. http://ora.ox.ac.uk/objects/uuid:5935d96d-625a-4778-b42d-bb56c96d96cc.
Повний текст джерелаWeiler, Florian [Verfasser], Horst [Akademischer Betreuer] Hahn, Horst [Gutachter] Hahn, Lars [Gutachter] Linsen, Bernhard [Gutachter] Preim, and Jan [Gutachter] Klein. "Computational tools for objective assessment in Neuroimaging / Florian Weiler ; Gutachter: Horst Hahn, Lars Linsen, Bernhard Preim, Jan Klein ; Betreuer: Horst Hahn." Bremen : IRC-Library, Information Resource Center der Jacobs University Bremen, 2020. http://d-nb.info/1203875983/34.
Повний текст джерелаIyappan, Anandhi [Verfasser]. "Conceptualization of computational modeling approaches and interpretation of the role of neuroimaging indices in pathomechanisms for pre-clinical detection of Alzheimer Disease / Anandhi Iyappan." Bonn : Universitäts- und Landesbibliothek Bonn, 2018. http://d-nb.info/1173789685/34.
Повний текст джерелаGloaguen, Arnaud. "A statistical and computational framework for multiblock and multiway data analysis." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG016.
Повний текст джерелаA challenging problem in multivariate statistics is to study relationships between several sets of variables measured on the same set of individuals. In the literature, this paradigm can be stated under several names as “learning from multimodal data”, “data integration”, “data fusion” or “multiblock data analysis”. Typical examples are found in a large variety of fields such as biology, chemistry, sensory analysis, marketing, food research, where the common general objective is to identify variables of each block that are active in the relationships with other blocks. Moreover, each block can be composed of a high number of measurements (~1M), which involves the computation of billion(s) of associations. A successful investigation of such a dataset requires developing a computational and statistical framework that fits both the peculiar structure of the data as well as its heterogeneous nature.The development of multivariate statistical methods constitutes the core of this work. All these developments find their foundations on Regularized Generalized Canonical Correlation Analysis (RGCCA), a flexible framework for multiblock data analysis that grasps in a single optimization problem many well known multiblock methods. The RGCCA algorithm consists in a single yet very simple update repeated until convergence. If this update is gifted with certain conditions, the global convergence of the procedure is guaranteed. Throughout this work, the optimization framework of RGCCA has been extended in several directions:(i) From sequential to global. We extend RGCCA from a sequential procedure to a global one by extracting all the block components simultaneously with a single optimization problem.(ii) From matrix to higher order tensors. Multiway Generalized Canonical Correlation Analysis (MGCCA) has been proposed as an extension of RGCCA to higher order tensors. Sequential and global strategies have been designed for extracting several components per block. The different variants of the MGCCA algorithm are globally convergent under mild conditions.(iii) From sparsity to structured sparsity. The core of the Sparse Generalized Canonical Correlation Analysis (SGCCA) algorithm has been improved. It provides a much faster globally convergent algorithm. SGCCA has been extended to handle structured sparse penalties.In the second part, the versatility and usefulness of the proposed methods have been investigated on various studies: (i) two imaging-genetic studies, (ii) two Electroencephalography studies and (iii) one Raman Microscopy study. For these analyses, the focus is made on the interpretation of the results eased by considering explicitly the multiblock, tensor and sparse structures
Книги з теми "Computational neuroimaging"
Li, Ping, and Hua Shu. Language and the brain: computational and neuroimaging evidence from Chinese. Oxford University Press, 2010. http://dx.doi.org/10.1093/oxfordhb/9780199541850.013.0007.
Повний текст джерелаTang, Xiaoying, Thomas Fletcher, and Michael I. Miller, eds. Bayesian Estimation and Inference in Computational Anatomy and Neuroimaging: Methods & Applications. Frontiers Media SA, 2019. http://dx.doi.org/10.3389/978-2-88945-984-1.
Повний текст джерелаSensory Nervous System - Computational Neuroimaging Investigations of Topographical Organization in Human Sensory Cortex [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.98172.
Повний текст джерелаAdams, Reginald B., Daniel N. Albohn, and Kestutis Kveraga. A Social Vision Account of Facial Expression Perception. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190613501.003.0017.
Повний текст джерелаChung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.
Знайти повний текст джерелаStatistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.
Знайти повний текст джерелаChung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.
Знайти повний текст джерелаChung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.
Знайти повний текст джерелаRoggeman, Chantal, Wim Fias, and Tom Verguts. Basic Number Representation and Beyond. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.68.
Повний текст джерелаShaikh, Mohd Faraz. Machine Learning in Detecting Auditory Sequences in Magnetoencephalography Data : Research Project in Computational Modelling and Simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.25368/2022.411.
Повний текст джерелаЧастини книг з теми "Computational neuroimaging"
Hanson, Stephen José, Michiro Negishi, and Catherine Hanson. "Connectionist Neuroimaging." In Emergent Neural Computational Architectures Based on Neuroscience, 560–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44597-8_40.
Повний текст джерелаLahmiri, Salim, Mounir Boukadoum, and Antonio Di Ieva. "Fractals in Neuroimaging." In Springer Series in Computational Neuroscience, 295–309. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3995-4_19.
Повний текст джерелаRay, Kimberly, and Angela Marie Richmond Laird. "Meta-analysis in Neuroimaging." In Encyclopedia of Computational Neuroscience, 1687–89. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_542.
Повний текст джерелаRay, Kimberly, and Angela Laird. "Meta-analysis in Neuroimaging." In Encyclopedia of Computational Neuroscience, 1–3. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_542-1.
Повний текст джерелаKawato, Mitsuo. "Brain-Machine Interface and Neuroimaging." In Encyclopedia of Computational Neuroscience, 441–43. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_523.
Повний текст джерелаPoline, Jean Baptiste, and David Kennedy. "Software for Neuroimaging Data Analysis." In Encyclopedia of Computational Neuroscience, 2733–44. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_538.
Повний текст джерелаOzaki, Tohru. "Statistical Analysis of Neuroimaging Data." In Encyclopedia of Computational Neuroscience, 2868–70. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_539.
Повний текст джерелаBojak, Ingo, and Michael Breakspear. "Neuroimaging, Neural Population Models for." In Encyclopedia of Computational Neuroscience, 1919–44. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_70.
Повний текст джерелаKawato, Mitsuo. "Brain Machine Interface and Neuroimaging." In Encyclopedia of Computational Neuroscience, 1–3. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_523-1.
Повний текст джерелаPoline, Jean Baptiste, and David Kennedy. "Software for Neuroimaging Data Analysis." In Encyclopedia of Computational Neuroscience, 1–14. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_538-1.
Повний текст джерелаТези доповідей конференцій з теми "Computational neuroimaging"
Wandell, Brian A., and Robert F. Dougherty. "Computational neuroimaging: maps and tracks in the human brain." In Electronic Imaging 2006, edited by Bernice E. Rogowitz, Thrasyvoulos N. Pappas, and Scott J. Daly. SPIE, 2006. http://dx.doi.org/10.1117/12.674141.
Повний текст джерелаMayrand, Robin Perry, Christian Yaphet Freytes, Luana Okino Sawada, Micheal Adeyosoye, Rosie E. Curiel Cid, David Lowenstein, Ranjan Duara, and Malek Adjouadi. "Computational Analysis of a Light-Weight SUVr Processing Technique for Neuroimaging Alzheimer's Disease." In 2022 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2022. http://dx.doi.org/10.1109/csci58124.2022.00317.
Повний текст джерелаSteinkamp, Simon, Iyadh Chaker, Félix Hubert, David Meder, and Oliver Hulme. "Computational Parametric Mapping: A Method For Mapping Cognitive Models Onto Neuroimaging Data." In 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1124-0.
Повний текст джерелаBaykova, Reny, and Warrick Roseboom. "Effects of Sensory Precision on Behavioral and Neuroimaging Perceptual Biases in Duration Estimation." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1280-0.
Повний текст джерелаObafemi-Ajayi, Tayo, Khalid Al-Jabery, Lauren Salminen, David Laidlaw, Ryan Cabeen, Donald Wunsch, and Robert Paul. "Neuroimaging biomarkers of cognitive decline in healthy older adults via unified learning." In 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017. http://dx.doi.org/10.1109/ssci.2017.8280937.
Повний текст джерелаKemtur, Anirudha, Francois Paugam, Basile Pinsard, Pravish sainath, Yann Harel, Maximilien Le clei, Julie Boyle, Karim Jerbi, and Pierre Bellec. "AI-based modeling of brain and behavior: Combining neuroimaging, imitation learning and video games." In 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1303-0.
Повний текст джерелаThomas, Armin, Hauke R. Heekeren, Klaus-Robert Müller, and Wojciech Samek. "DeepLight: A Structured Framework For The Analysis of Neuroimaging Data Through Recurrent Deep Learning Models." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1226-0.
Повний текст джерелаEnyagina, I., A. Polyakov, and D. Kokovin. "SERVICES OF COMPUTATIONAL NEUROBIOLOGY TASKS, BASED ON THE DISTRIBUTED MODULAR PLATFORM «DIGITAL LABORATORY» NRC «KURCHATOV INSTITUTE»." In 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". Crossref, 2021. http://dx.doi.org/10.54546/mlit.2021.13.75.001.
Повний текст джерелаNiu, Xin, Hualou Liang, and Fengqing Zhang. "Brain age prediction for post-traumatic stress disorder patients with convolutional neural networks: a multi-modal neuroimaging study." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1121-0.
Повний текст джерелаGarimella, Harsha T., and Reuben H. Kraft. "Validation of Embedded Element Method in the Prediction of White Matter Disruption in Concussions." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67785.
Повний текст джерелаЗвіти організацій з теми "Computational neuroimaging"
Cohen, Jonathan D. Second Generation Flexible Computing Environment for Computational Modeling of Brain Function and Neuroimaging Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada530764.
Повний текст джерела