Literatura académica sobre el tema "COMPUTATIONAL NEUROSCIENCE MODELS"
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Artículos de revistas sobre el tema "COMPUTATIONAL NEUROSCIENCE MODELS"
Krasovskaya, Sofia y W. Joseph MacInnes. "Salience Models: A Computational Cognitive Neuroscience Review". Vision 3, n.º 4 (25 de octubre de 2019): 56. http://dx.doi.org/10.3390/vision3040056.
Texto completoBisht, Raj Kishor. "Design and Development of Mathematical Models for Computational Neuroscience". Mathematical Statistician and Engineering Applications 70, n.º 1 (31 de enero de 2021): 612–20. http://dx.doi.org/10.17762/msea.v70i1.2515.
Texto completoMartin, Andrea E. "A Compositional Neural Architecture for Language". Journal of Cognitive Neuroscience 32, n.º 8 (agosto de 2020): 1407–27. http://dx.doi.org/10.1162/jocn_a_01552.
Texto completoChirimuuta, M. "Minimal models and canonical neural computations: the distinctness of computational explanation in neuroscience". Synthese 191, n.º 2 (27 de noviembre de 2013): 127–53. http://dx.doi.org/10.1007/s11229-013-0369-y.
Texto completoFellous, Jean-Marc y Christiane Linster. "Computational Models of Neuromodulation". Neural Computation 10, n.º 4 (1 de mayo de 1998): 771–805. http://dx.doi.org/10.1162/089976698300017476.
Texto completoMigliore, Michele, Thomas M. Morse, Andrew P. Davison, Luis Marenco, Gordon M. Shepherd y Michael L. Hines. "ModelDB: Making Models Publicly Accessible to Support Computational Neuroscience". Neuroinformatics 1, n.º 1 (2003): 135–40. http://dx.doi.org/10.1385/ni:1:1:135.
Texto completoJiang, Weihang. "Applications of machine learning in neuroscience and inspiration of reinforcement learning for computational neuroscience". Applied and Computational Engineering 4, n.º 1 (14 de junio de 2023): 473–78. http://dx.doi.org/10.54254/2755-2721/4/2023308.
Texto completoGardner, Justin L. y Elisha P. Merriam. "Population Models, Not Analyses, of Human Neuroscience Measurements". Annual Review of Vision Science 7, n.º 1 (15 de septiembre de 2021): 225–55. http://dx.doi.org/10.1146/annurev-vision-093019-111124.
Texto completoGrindrod, Peter y Desmond J. Higham. "Evolving graphs: dynamical models, inverse problems and propagation". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 466, n.º 2115 (11 de noviembre de 2009): 753–70. http://dx.doi.org/10.1098/rspa.2009.0456.
Texto completoGisiger, T. "Computational models of association cortex". Current Opinion in Neurobiology 10, n.º 2 (1 de abril de 2000): 250–59. http://dx.doi.org/10.1016/s0959-4388(00)00075-1.
Texto completoTesis sobre el tema "COMPUTATIONAL NEUROSCIENCE MODELS"
Marsh, Steven Joseph Thomas. "Efficient programming models for neurocomputation". Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709268.
Texto completoZhu, Mengchen. "Sparse coding models of neural response in the primary visual cortex". Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53868.
Texto completoFöldiak, Peter. "Models of sensory coding". Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239097.
Texto completoWoldman, Wessel. "Emergent phenomena from dynamic network models : mathematical analysis of EEG from people with IGE". Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/23297.
Texto completoMender, Bedeho M. W. "Models of primate supraretinal visual representations". Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:ce1fff8e-db5c-46e4-b5aa-7439465c2a77.
Texto completoShepardson, Dylan. "Algorithms for inverting Hodgkin-Huxley type neuron models". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31686.
Texto completoCommittee Chair: Tovey, Craig; Committee Member: Butera, Rob; Committee Member: Nemirovski, Arkadi; Committee Member: Prinz, Astrid; Committee Member: Sokol, Joel. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Boatin, William. "Characterization of neuron models". Thesis, Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-04182005-181732/.
Texto completoDr. Robert H. Lee, Committee Member ; Dr. Kurt Wiesenfeld, Committee Member ; Dr Robert J. Butera, Committee Member.
BIDDELL, KEVIN MICHAEL. "CREATION OF A BIOPHYSICAL MODEL OF A STRIATAL DORSAL LATERAL MEDIUM SPINY NEURON INCORPORATING DENDRITIC EXCITATION BY NMDA AND AMPA RECEPTOR MODELS". University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196211076.
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.
Hendrickson, Eric B. "Morphologically simplified conductance based neuron models: principles of construction and use in parameter optimization". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33905.
Texto completoLibros sobre el tema "COMPUTATIONAL NEUROSCIENCE MODELS"
Hecht-Nielsen, Robert y Thomas McKenna, eds. Computational Models for Neuroscience. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0085-0.
Texto completoL, Schwartz Eric, ed. Computational neuroscience. Cambridge, Mass: MIT Press, 1990.
Buscar texto completoComputational neuroscience. Cambridge, Mass: MIT Press, 1990.
Buscar texto completoPardalos, P. M. Computational neuroscience. New York: Springer, 2010.
Buscar texto completoA, Ascoli Georgio, ed. Computational neuroanatomy: Principles and methods. Totowa, N.J: Humana Press, 2002.
Buscar texto completoChakravarthy, V. Srinivasa y Ahmed A. Moustafa. Computational Neuroscience Models of the Basal Ganglia. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8494-2.
Texto completode, Schutter Erik, ed. Computational modeling methods for neuroscientists. Cambridge, Mass: MIT Press, 2010.
Buscar texto completoFundamentals of computational neuroscience. 2a ed. Oxford: Oxford University Press, 2010.
Buscar texto completoTrappenberg, Thomas P. Fundamentals of computational neuroscience. 2a ed. Oxford: Oxford University Press, 2010.
Buscar texto completoTrappenberg, Thomas P. Fundamentals of computational neuroscience. 2a ed. Oxford: Oxford University Press, 2010.
Buscar texto completoCapítulos de libros sobre el tema "COMPUTATIONAL NEUROSCIENCE MODELS"
van Gils, Stephan y Wim van Drongelen. "Epilepsy: Computational Models". En Encyclopedia of Computational Neuroscience, 1121–34. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_504.
Texto completovan Gils, Stephan y Wim van Drongelen. "Epilepsy: Computational Models". En Encyclopedia of Computational Neuroscience, 1–17. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7320-6_504-1.
Texto completoSkinner, Frances K., Nancy Kopell y Brian Mulloney. "Mathematical Models of the Crayfish Swimmeret System". En Computational Neuroscience, 839–43. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-9800-5_130.
Texto completoLee, D. D., B. Y. Reis, H. S. Seung y D. W. Tank. "Nonlinear Network Models of the Oculomotor Integrator". En Computational Neuroscience, 371–77. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-9800-5_60.
Texto completoLansner, Anders, Örjan Ekeberg, Erik Fransén, Per Hammarlund y Tomas Wilhelmsson. "Detailed Simulation of Large Scale Neural Network Models". En Computational Neuroscience, 931–35. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-9800-5_144.
Texto completoBednar, James A. y Risto Miikkulainen. "Pattern-Generator-Driven Development in Self-Organizing Models". En Computational Neuroscience, 317–23. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-4831-7_53.
Texto completoCheong, Jin Hyun, Eshin Jolly, Sunhae Sul y Luke J. Chang. "Computational Models in Social Neuroscience". En Computational Models of Brain and Behavior, 229–44. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119159193.ch17.
Texto completoYu, Angela J. "Computational Models of Neuromodulation". En Encyclopedia of Computational Neuroscience, 761–66. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_625.
Texto completoYu, Angela J. "Computational Models of Neuromodulation". En Encyclopedia of Computational Neuroscience, 1–6. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_625-1.
Texto completoJardri, Renaud y Sophie Denève. "Computational Models of Hallucinations". En The Neuroscience of Hallucinations, 289–313. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4121-2_16.
Texto completoActas de conferencias sobre el tema "COMPUTATIONAL NEUROSCIENCE MODELS"
Tirupattur, Naveen, Christopher C. Lapish, Snehasis Mukhopadhyay, Tuan D. Pham, Xiaobo Zhou, Hiroshi Tanaka, Mayumi Oyama-Higa et al. "Text Mining for Neuroscience". En 2011 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MODELS FOR LIFE SCIENCES (CMLS-11). AIP, 2011. http://dx.doi.org/10.1063/1.3596634.
Texto completoMohnert, Florian, Mateo Tošić y Falk Lieder. "Testing Computational Models of Goal Pursuit". En 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1350-0.
Texto completoUchiyama, Ryutaro, Claudio Tennie y Charley Wu. "Model-Based Assimilation Transmits and Recombines World Models". En 2023 Conference on Cognitive Computational Neuroscience. Oxford, United Kingdom: Cognitive Computational Neuroscience, 2023. http://dx.doi.org/10.32470/ccn.2023.1722-0.
Texto completoMuzellec, Sabine, Mathieu Chalvidal, Thomas Serre y Rufin VanRullen. "Accurate implementation of computational neuroscience models through neural ODEs". En 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1165-0.
Texto completoBelledonne, Mario, Chloë Geller y Ilker Yildirim. "Goal-conditioned world models: Adaptive computation over multi-granular generative models explains human scene perception". En 2023 Conference on Cognitive Computational Neuroscience. Oxford, United Kingdom: Cognitive Computational Neuroscience, 2023. http://dx.doi.org/10.32470/ccn.2023.1663-0.
Texto completoSpeekenbrink, Maarten. "Identifiability of Gaussian Bayesian bandit models". En 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1335-0.
Texto completoBaltieri, Manuel y Christopher L. Buckley. "Active Inference: Computational Models of Motor Control without Efference Copy". En 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1144-0.
Texto completoYao, Yuanwei, Tatia Buidze y Jan Gläscher. "Effectiveness of different computational models during the Tacit Communication Game". En 2023 Conference on Cognitive Computational Neuroscience. Oxford, United Kingdom: Cognitive Computational Neuroscience, 2023. http://dx.doi.org/10.32470/ccn.2023.1268-0.
Texto completoWeidinger, Laura, Andrea Gradassi, Lucas Molleman y Wouter van den Bos. "Test-retest reliability of canonical reinforcement learning models". En 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1053-0.
Texto completoCeja, Vanessa, Yussuf Ezzeldine y Megan A. K. Peters. "Models of confidence to facilitate engaging task designs". En 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1150-0.
Texto completoInformes sobre el tema "COMPUTATIONAL NEUROSCIENCE MODELS"
Semerikov, Serhiy O., Illia O. Teplytskyi, Yuliia V. Yechkalo y Arnold E. Kiv. Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. [б. в.], noviembre de 2018. http://dx.doi.org/10.31812/123456789/2648.
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