Literatura académica sobre el tema "Computational neuroimaging, cognitive neuroscience"
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Artículos de revistas sobre el tema "Computational neuroimaging, cognitive neuroscience"
Medaglia, John D., Mary-Ellen Lynall y Danielle S. Bassett. "Cognitive Network Neuroscience". Journal of Cognitive Neuroscience 27, n.º 8 (agosto de 2015): 1471–91. http://dx.doi.org/10.1162/jocn_a_00810.
Texto completoMcIntosh, Randy, Sean Hill y Olaf Sporns. "Editorial: Focus feature on consciousness and cognition". Network Neuroscience 6, n.º 4 (2022): 934–36. http://dx.doi.org/10.1162/netn_e_00273.
Texto completoRudrauf, David. "Structure-Function Relationships behind the Phenomenon of Cognitive Resilience in Neurology: Insights for Neuroscience and Medicine". Advances in Neuroscience 2014 (4 de agosto de 2014): 1–28. http://dx.doi.org/10.1155/2014/462765.
Texto completoNadel, L., A. Samsonovich, L. Ryan y M. Moscovitch. "Multiple trace theory of human memory: Computational, neuroimaging, and neuropsychological results". Hippocampus 10, n.º 4 (2000): 352–68. http://dx.doi.org/10.1002/1098-1063(2000)10:4<352::aid-hipo2>3.0.co;2-d.
Texto completoZmigrod, Leor y Manos Tsakiris. "Computational and neurocognitive approaches to the political brain: key insights and future avenues for political neuroscience". Philosophical Transactions of the Royal Society B: Biological Sciences 376, n.º 1822 (22 de febrero de 2021): 20200130. http://dx.doi.org/10.1098/rstb.2020.0130.
Texto completoPopal, Haroon, Yin Wang y Ingrid R. Olson. "A Guide to Representational Similarity Analysis for Social Neuroscience". Social Cognitive and Affective Neuroscience 14, n.º 11 (1 de noviembre de 2019): 1243–53. http://dx.doi.org/10.1093/scan/nsz099.
Texto completoChatham, Christopher H., Seth A. Herd, Angela M. Brant, Thomas E. Hazy, Akira Miyake, Randy O'Reilly y Naomi P. Friedman. "From an Executive Network to Executive Control: A Computational Model of the n-back Task". Journal of Cognitive Neuroscience 23, n.º 11 (noviembre de 2011): 3598–619. http://dx.doi.org/10.1162/jocn_a_00047.
Texto completoMujica-Parodi, Lilianne R. y Helmut H. Strey. "Making Sense of Computational Psychiatry". International Journal of Neuropsychopharmacology 23, n.º 5 (27 de marzo de 2020): 339–47. http://dx.doi.org/10.1093/ijnp/pyaa013.
Texto completoParkinson, Carolyn. "Computational methods in social neuroscience: recent advances, new tools and future directions". Social Cognitive and Affective Neuroscience 16, n.º 8 (24 de junio de 2021): 739–44. http://dx.doi.org/10.1093/scan/nsab073.
Texto completoRojek-Giffin, Michael, Mael Lebreton, H. Steven Scholte, Frans van Winden, K. Richard Ridderinkhof y Carsten K. W. De Dreu. "Neurocognitive Underpinnings of Aggressive Predation in Economic Contests". Journal of Cognitive Neuroscience 32, n.º 7 (julio de 2020): 1276–88. http://dx.doi.org/10.1162/jocn_a_01545.
Texto completoTesis sobre el tema "Computational neuroimaging, cognitive neuroscience"
Salimi-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.
Texto completoCooke, Megan E. "Integrating Genetics and Neuroimaging to study Subtypes of Binge Drinkers". VCU Scholars Compass, 2017. https://scholarscompass.vcu.edu/etd/5167.
Texto completoCronin, Beau D. "Quantifying uncertainty in computational neuroscience with Bayesian statistical inference". Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45336.
Texto completoIncludes bibliographical references (p. 101-106).
Two key fields of computational neuroscience involve, respectively, the analysis of experimental recordings to understand the functional properties of neurons, and modeling how neurons and networks process sensory information in order to represent the environment. In both of these endeavors, it is crucial to understand and quantify uncertainty - when describing how the brain itself draws conclusions about the physical world, and when the experimenter interprets neuronal data. Bayesian modeling and inference methods provide many advantages for doing so. Three projects are presented that illustrate the advantages of the Bayesian approach. In the first, Markov chain Monte Carlo (MCMC) sampling methods were used to answer a range of scientific questions that arise in the analysis of physiological data from tuning curve experiments; in addition, a software toolbox is described that makes these methods widely accessible. In the second project, the model developed in the first project was extended to describe the detailed dynamics of orientation tuning in neurons in cat primary visual cortex. Using more sophisticated sampling-based inference methods, this model was applied to answer specific scientific questions about the tuning properties of a recorded population. The final project uses a Bayesian model to provide a normative explanation of sensory adaptation phenomena. The model was able to explain a range of detailed physiological adaptation phenomena.
by Beau D. Cronin.
Ph.D.
Lundh, Dan. "A computational neuroscientific model for short-term memory". Thesis, University of Exeter, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324742.
Texto completoVellmer, Sebastian. "Applications of the Fokker-Planck Equation in Computational and Cognitive Neuroscience". Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21597.
Texto completoThis thesis is concerned with the calculation of statistics, in particular the power spectra, of point processes generated by stochastic multidimensional integrate-and-fire (IF) neurons, networks of IF neurons and decision-making models from the corresponding Fokker-Planck equations. In the brain, information is encoded by sequences of action potentials. In studies that focus on spike timing, IF neurons that drastically simplify the spike generation have become the standard model. One-dimensional IF neurons do not suffice to accurately model neural dynamics, however, the extension towards multiple dimensions yields realistic behavior at the price of growing complexity. The first part of this work develops a theory of spike-train power spectra for stochastic, multidimensional IF neurons. From the Fokker-Planck equation, a set of partial differential equations is derived that describes the stationary probability density, the firing rate and the spike-train power spectrum. In the second part of this work, a mean-field theory of large and sparsely connected homogeneous networks of spiking neurons is developed that takes into account the self-consistent temporal correlations of spike trains. Neural input is approximated by colored Gaussian noise generated by a multidimensional Ornstein-Uhlenbeck process of which the coefficients are initially unknown but determined by the self-consistency condition and define the solution of the theory. To explore heterogeneous networks, an iterative scheme is extended to determine the distribution of spectra. In the third part, the Fokker-Planck equation is applied to calculate the statistics of sequences of binary decisions from diffusion-decision models (DDM). For the analytically tractable DDM, the statistics are calculated from the corresponding Fokker-Planck equation. To determine the statistics for nonlinear models, the threshold-integration method is generalized.
Cattinelli, I. "INVESTIGATIONS ON COGNITIVE COMPUTATION AND COMPUTATIONAL COGNITION". Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155482.
Texto completoPetitet, 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.
Texto completoWright, Sean Patrick. "Cognitive neuroscience of episodic memory: behavioral, genetic, electrophysiological, and computational approaches to sequence memory". Thesis, Boston University, 2003. https://hdl.handle.net/2144/27805.
Texto completoPLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.
2031-01-02
Vellmer, Sebastian [Verfasser]. "Applications of the Fokker-Planck Equation in Computational and Cognitive Neuroscience / Sebastian Vellmer". Berlin : Humboldt-Universität zu Berlin, 2020. http://d-nb.info/1214240682/34.
Texto completoGing-Jehli, Nadja Rita. "On the implementation of Computational Psychiatry within the framework of Cognitive Psychology and Neuroscience". The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555338342285251.
Texto completoLibros sobre el tema "Computational neuroimaging, cognitive neuroscience"
Zhao, Qi, ed. Computational and Cognitive Neuroscience of Vision. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-0213-7.
Texto completoGallistel, C. R. Memory and the computational brain: Why cognitive science will transform neuroscience. Chichester, West Sussex, UK: Wiley-Blackwell, 2009.
Buscar texto completoFrank, Rösler, ed. Neuroimaging of human memory: Linking cognitive processes to neural systems. Oxford: Oxford University Press, 2009.
Buscar texto completoFrank, Rösler, ed. Neuroimaging of human memory: Linking cognitive processes to neural systems. Oxford: Oxford University Press, 2009.
Buscar texto completoG, Hillary Frank y DeLuca John 1956-, eds. Functional neuroimaging in clinical populations. New York: Guilford Press, 2007.
Buscar texto completoBrain-inspired Cognitive Systems Conference (2010 : Madrid, Spain). From brains to systems: Brain-inspired cognitive systems 2010. Editado por Hernández Carlos. New York: Springer, 2011.
Buscar texto completoRoberto, Cabeza y Kingstone Alan, eds. Handbook of functional neuroimaging of cognition. 2a ed. Cambridge, MA: MIT Press, 2005.
Buscar texto completoInternational Conference on Intelligent Computing (3rd 2007 Qingdao, China). Advanced intelligent computing theories and applications: With aspects of artifical intelligence ; third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007 ; proceedings. Berlin: Springer, 2007.
Buscar texto completoW, Cottrell Garrison, ed. Proceedings of the eighteenth annual conference of the Cognitive Science Society: July 12-15, 1996, University of California, San Diego. Mahwah, N.J: Lawrence Erlbaum Associates, 1996.
Buscar texto completoDe-Shuang, Huang, Li Kang y Irwin G. W. 1950-, eds. International Conference on Intelligent Computing: ICIC 2006, Kunming, China, August 16-19, 2006 : proceedings. Berlin: Springer, 2006.
Buscar texto completoCapítulos de libros sobre el tema "Computational neuroimaging, cognitive neuroscience"
Ray, Kimberly y Angela Marie Richmond Laird. "Meta-analysis in Neuroimaging". En 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.
Texto completoRay, Kimberly y Angela Laird. "Meta-analysis in Neuroimaging". En 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.
Texto completoLahmiri, Salim, Mounir Boukadoum y Antonio Di Ieva. "Fractals in Neuroimaging". En 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.
Texto completoShimosegawa, Eku. "Advances in Neuroimaging Techniques with PET". En Cognitive Neuroscience Robotics B, 171–87. Tokyo: Springer Japan, 2016. http://dx.doi.org/10.1007/978-4-431-54598-9_8.
Texto completoZednik, Carlos. "Computational cognitive neuroscience". En The Routledge Handbook of the Computational Mind, 357–69. Milton Park, Abingdon, Oxon ; New York : Routledge, 2019. |: Routledge, 2018. http://dx.doi.org/10.4324/9781315643670-27.
Texto completoKawato, Mitsuo. "Brain-Machine Interface and Neuroimaging". En 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.
Texto completoPoline, Jean Baptiste y David Kennedy. "Software for Neuroimaging Data Analysis". En 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.
Texto completoOzaki, Tohru. "Statistical Analysis of Neuroimaging Data". En 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.
Texto completoBojak, Ingo y Michael Breakspear. "Neuroimaging, Neural Population Models for". En 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.
Texto completoKawato, Mitsuo. "Brain Machine Interface and Neuroimaging". En 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.
Texto completoActas de conferencias sobre el tema "Computational neuroimaging, cognitive neuroscience"
Steinkamp, Simon, Iyadh Chaker, Félix Hubert, David Meder y Oliver Hulme. "Computational Parametric Mapping: A Method For Mapping Cognitive Models Onto Neuroimaging Data". En 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1124-0.
Texto completoBaykova, Reny y Warrick Roseboom. "Effects of Sensory Precision on Behavioral and Neuroimaging Perceptual Biases in Duration Estimation". En 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1280-0.
Texto completoKemtur, Anirudha, Francois Paugam, Basile Pinsard, Pravish sainath, Yann Harel, Maximilien Le clei, Julie Boyle, Karim Jerbi y Pierre Bellec. "AI-based modeling of brain and behavior: Combining neuroimaging, imitation learning and video games". En 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1303-0.
Texto completoThomas, Armin, Hauke R. Heekeren, Klaus-Robert Müller y Wojciech Samek. "DeepLight: A Structured Framework For The Analysis of Neuroimaging Data Through Recurrent Deep Learning Models". En 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1226-0.
Texto completoNiu, Xin, Hualou Liang y Fengqing Zhang. "Brain age prediction for post-traumatic stress disorder patients with convolutional neural networks: a multi-modal neuroimaging study". En 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1121-0.
Texto completoSchrouff, J. y J. Mourao-Miranda. "Interpreting weight maps in terms of cognitive or clinical neuroscience: nonsense?" En 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE, 2018. http://dx.doi.org/10.1109/prni.2018.8423944.
Texto completoPark, Seongmin, Maryam Zolfaghar, Jacob Russin, Douglas Miller, Randall O’Reilly y Erie Boorman. "The geometry of cognitive maps under dynamic cognitive control". En 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1023-0.
Texto completoMaley, Corey. "Analog Computation in Computational Cognitive Neuroscience". En 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1178-0.
Texto completoF. da Costa, Pedro, Sebastian Popescu, Robert Leech y Romy Lorenz. "Elucidating Cognitive Processes Using LSTMs". En 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1201-0.
Texto completoWegner, Katharina, Charlie Wilson, Emannuel Procyk, Karl Friston y Daniele Marinazzo. "Cognitive Effort Modulates Frontal Effective Connections". En 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1232-0.
Texto completoInformes sobre el tema "Computational neuroimaging, cognitive neuroscience"
Schunn, C. D. A Review of Human Spatial Representations Computational, Neuroscience, Mathematical, Developmental, and Cognitive Psychology Considerations. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2000. http://dx.doi.org/10.21236/ada440864.
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