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Статті в журналах з теми "Computational neuroimaging, cognitive neuroscience"
Medaglia, John D., Mary-Ellen Lynall, and Danielle S. Bassett. "Cognitive Network Neuroscience." Journal of Cognitive Neuroscience 27, no. 8 (August 2015): 1471–91. http://dx.doi.org/10.1162/jocn_a_00810.
Повний текст джерелаMcIntosh, Randy, Sean Hill, and Olaf Sporns. "Editorial: Focus feature on consciousness and cognition." Network Neuroscience 6, no. 4 (2022): 934–36. http://dx.doi.org/10.1162/netn_e_00273.
Повний текст джерелаRudrauf, David. "Structure-Function Relationships behind the Phenomenon of Cognitive Resilience in Neurology: Insights for Neuroscience and Medicine." Advances in Neuroscience 2014 (August 4, 2014): 1–28. http://dx.doi.org/10.1155/2014/462765.
Повний текст джерелаNadel, L., A. Samsonovich, L. Ryan, and M. Moscovitch. "Multiple trace theory of human memory: Computational, neuroimaging, and neuropsychological results." Hippocampus 10, no. 4 (2000): 352–68. http://dx.doi.org/10.1002/1098-1063(2000)10:4<352::aid-hipo2>3.0.co;2-d.
Повний текст джерелаZmigrod, Leor, and 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, no. 1822 (February 22, 2021): 20200130. http://dx.doi.org/10.1098/rstb.2020.0130.
Повний текст джерелаPopal, Haroon, Yin Wang, and Ingrid R. Olson. "A Guide to Representational Similarity Analysis for Social Neuroscience." Social Cognitive and Affective Neuroscience 14, no. 11 (November 1, 2019): 1243–53. http://dx.doi.org/10.1093/scan/nsz099.
Повний текст джерелаChatham, Christopher H., Seth A. Herd, Angela M. Brant, Thomas E. Hazy, Akira Miyake, Randy O'Reilly, and Naomi P. Friedman. "From an Executive Network to Executive Control: A Computational Model of the n-back Task." Journal of Cognitive Neuroscience 23, no. 11 (November 2011): 3598–619. http://dx.doi.org/10.1162/jocn_a_00047.
Повний текст джерелаMujica-Parodi, Lilianne R., and Helmut H. Strey. "Making Sense of Computational Psychiatry." International Journal of Neuropsychopharmacology 23, no. 5 (March 27, 2020): 339–47. http://dx.doi.org/10.1093/ijnp/pyaa013.
Повний текст джерелаParkinson, Carolyn. "Computational methods in social neuroscience: recent advances, new tools and future directions." Social Cognitive and Affective Neuroscience 16, no. 8 (June 24, 2021): 739–44. http://dx.doi.org/10.1093/scan/nsab073.
Повний текст джерелаRojek-Giffin, Michael, Mael Lebreton, H. Steven Scholte, Frans van Winden, K. Richard Ridderinkhof, and Carsten K. W. De Dreu. "Neurocognitive Underpinnings of Aggressive Predation in Economic Contests." Journal of Cognitive Neuroscience 32, no. 7 (July 2020): 1276–88. http://dx.doi.org/10.1162/jocn_a_01545.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаCooke, Megan E. "Integrating Genetics and Neuroimaging to study Subtypes of Binge Drinkers." VCU Scholars Compass, 2017. https://scholarscompass.vcu.edu/etd/5167.
Повний текст джерелаCronin, Beau D. "Quantifying uncertainty in computational neuroscience with Bayesian statistical inference." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45336.
Повний текст джерелаIncludes 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.
Повний текст джерелаVellmer, 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.
Повний текст джерелаThis 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.
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 джерелаWright, 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.
Повний текст джерелаPLEASE 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.
Повний текст джерелаGing-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.
Повний текст джерелаКниги з теми "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.
Повний текст джерелаGallistel, C. R. Memory and the computational brain: Why cognitive science will transform neuroscience. Chichester, West Sussex, UK: Wiley-Blackwell, 2009.
Знайти повний текст джерелаFrank, Rösler, ed. Neuroimaging of human memory: Linking cognitive processes to neural systems. Oxford: Oxford University Press, 2009.
Знайти повний текст джерелаFrank, Rösler, ed. Neuroimaging of human memory: Linking cognitive processes to neural systems. Oxford: Oxford University Press, 2009.
Знайти повний текст джерелаG, Hillary Frank, and DeLuca John 1956-, eds. Functional neuroimaging in clinical populations. New York: Guilford Press, 2007.
Знайти повний текст джерелаBrain-inspired Cognitive Systems Conference (2010 : Madrid, Spain). From brains to systems: Brain-inspired cognitive systems 2010. Edited by Hernández Carlos. New York: Springer, 2011.
Знайти повний текст джерелаRoberto, Cabeza, and Kingstone Alan, eds. Handbook of functional neuroimaging of cognition. 2nd ed. Cambridge, MA: MIT Press, 2005.
Знайти повний текст джерелаInternational 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.
Знайти повний текст джерелаW, 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.
Знайти повний текст джерелаDe-Shuang, Huang, Li Kang, and Irwin G. W. 1950-, eds. International Conference on Intelligent Computing: ICIC 2006, Kunming, China, August 16-19, 2006 : proceedings. Berlin: Springer, 2006.
Знайти повний текст джерелаЧастини книг з теми "Computational neuroimaging, cognitive neuroscience"
Ray, 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 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 джерелаShimosegawa, Eku. "Advances in Neuroimaging Techniques with PET." In Cognitive Neuroscience Robotics B, 171–87. Tokyo: Springer Japan, 2016. http://dx.doi.org/10.1007/978-4-431-54598-9_8.
Повний текст джерелаZednik, Carlos. "Computational cognitive neuroscience." In 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.
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 джерелаТези доповідей конференцій з теми "Computational neuroimaging, cognitive neuroscience"
Steinkamp, 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 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 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 джерелаSchrouff, J., and J. Mourao-Miranda. "Interpreting weight maps in terms of cognitive or clinical neuroscience: nonsense?" In 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE, 2018. http://dx.doi.org/10.1109/prni.2018.8423944.
Повний текст джерелаPark, Seongmin, Maryam Zolfaghar, Jacob Russin, Douglas Miller, Randall O’Reilly, and Erie Boorman. "The geometry of cognitive maps under dynamic cognitive control." In 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1023-0.
Повний текст джерелаMaley, Corey. "Analog Computation in Computational Cognitive Neuroscience." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1178-0.
Повний текст джерелаF. da Costa, Pedro, Sebastian Popescu, Robert Leech, and Romy Lorenz. "Elucidating Cognitive Processes Using LSTMs." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1201-0.
Повний текст джерелаWegner, Katharina, Charlie Wilson, Emannuel Procyk, Karl Friston, and Daniele Marinazzo. "Cognitive Effort Modulates Frontal Effective Connections." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1232-0.
Повний текст джерелаЗвіти організацій з теми "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, December 2000. http://dx.doi.org/10.21236/ada440864.
Повний текст джерела