Academic literature on the topic 'Computational neuroscience'
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Journal articles on the topic "Computational neuroscience"
Cao, Jinde, Qingshan Liu, Sabri Arik, Jianlong Qiu, Haijun Jiang, and Ahmed Elaiw. "Computational Neuroscience." Computational and Mathematical Methods in Medicine 2014 (2014): 1–2. http://dx.doi.org/10.1155/2014/120280.
Full textSejnowski, T., C. Koch, and P. Churchland. "Computational neuroscience." Science 241, no. 4871 (September 9, 1988): 1299–306. http://dx.doi.org/10.1126/science.3045969.
Full textSejnowski, Terrence J. "Computational neuroscience." Behavioral and Brain Sciences 9, no. 1 (March 1986): 104–5. http://dx.doi.org/10.1017/s0140525x00021713.
Full textMoore, John W. "Computational Neuroscience." Contemporary Psychology: A Journal of Reviews 38, no. 2 (February 1993): 137–39. http://dx.doi.org/10.1037/033019.
Full textRingo, J. L. "Computational Neuroscience." Archives of Neurology 48, no. 2 (February 1, 1991): 130. http://dx.doi.org/10.1001/archneur.1991.00530140018008.
Full textKriegeskorte, Nikolaus, and Pamela K. Douglas. "Cognitive computational neuroscience." Nature Neuroscience 21, no. 9 (August 20, 2018): 1148–60. http://dx.doi.org/10.1038/s41593-018-0210-5.
Full textCecchi, Guillermo A., and James Kozloski. "Preface: Computational neuroscience." IBM Journal of Research and Development 61, no. 2/3 (March 1, 2017): 0:1–0:4. http://dx.doi.org/10.1147/jrd.2017.2690118.
Full textPopovych, Oleksandr, Peter Tass, and Christian Hauptmann. "Desynchronization (computational neuroscience)." Scholarpedia 6, no. 10 (2011): 1352. http://dx.doi.org/10.4249/scholarpedia.1352.
Full textÉrdi, Péter. "Teaching computational neuroscience." Cognitive Neurodynamics 9, no. 5 (March 21, 2015): 479–85. http://dx.doi.org/10.1007/s11571-015-9340-6.
Full textBecker, Suzanna, and Nathaniel D. Daw. "Computational cognitive neuroscience." Brain Research 1299 (November 2009): 1–2. http://dx.doi.org/10.1016/j.brainres.2009.09.114.
Full textDissertations / Theses on the topic "Computational neuroscience"
Higgins, Irina. "Computational neuroscience of speech recognition." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:daa8d096-6534-4174-b63e-cc4161291c90.
Full textWalters, Daniel Matthew. "The computational neuroscience of head direction cells." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:d4afe06a-d44f-4a24-99a3-d0e0a2911459.
Full textCronin, Beau D. "Quantifying uncertainty in computational neuroscience with Bayesian statistical inference." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45336.
Full textIncludes 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.
Stevens, Martin. "Animal camouflage, receiver psychology and the computational neuroscience of avian vision." Thesis, University of Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432958.
Full textTromans, James Matthew. "Computational neuroscience of natural scene processing in the ventral visual pathway." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:b82e1332-df7b-41db-9612-879c7a7dda39.
Full textVellmer, 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.
Full textThis 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.
Zhu, Mengchen. "Sparse coding models of neural response in the primary visual cortex." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53868.
Full textWoldman, 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.
Full textNguyen, Harrison Tri Tue. "Computational Neuroscience with Deep Learning for Brain Imaging Analysis and Behaviour Classification." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/27313.
Full textLundh, 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.
Full textBooks on the topic "Computational neuroscience"
Ribeiro, Paulo Rogério de Almeida, Vinícius Rosa Cota, Dante Augusto Couto Barone, and Alexandre César Muniz de Oliveira, eds. Computational Neuroscience. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08443-0.
Full textCota, Vinícius Rosa, Dante Augusto Couto Barone, Diego Roberto Colombo Dias, and Laila Cristina Moreira Damázio, eds. Computational Neuroscience. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36636-0.
Full textBower, James M., ed. Computational Neuroscience. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-9800-5.
Full textChaovalitwongse, Wanpracha, Panos M. Pardalos, and Petros Xanthopoulos, eds. Computational Neuroscience. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-0-387-88630-5.
Full textBarone, Dante Augusto Couto, Eduardo Oliveira Teles, and Christian Puhlmann Brackmann, eds. Computational Neuroscience. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71011-2.
Full textMallot, Hanspeter A. Computational Neuroscience. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00861-5.
Full textBower, James M., ed. Computational Neuroscience. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-4831-7.
Full textStoyanov, Drozdstoy, Bogdan Draganski, Paolo Brambilla, and Claus Lamm, eds. Computational Neuroscience. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3230-7.
Full textRiascos Salas, Jaime A., Vinícius Rosa Cota, Hernán Villota, and Daniel Betancur Vasquez, eds. Computational Neuroscience. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63848-0.
Full textPardalos, P. M. Computational neuroscience. New York: Springer, 2010.
Find full textBook chapters on the topic "Computational neuroscience"
Hasselmo, Michael E., and James R. Hinman. "Computational Neuroscience: Hippocampus." In Neuroscience in the 21st Century, 3081–95. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3474-4_175.
Full textHasselmo, Michael E., and James R. Hinman. "Computational Neuroscience: Hippocampus." In Neuroscience in the 21st Century, 1–15. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4614-6434-1_175-1.
Full textHasselmo, Michael E., and James R. Hinman. "Computational Neuroscience: Hippocampus." In Neuroscience in the 21st Century, 3489–503. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88832-9_175.
Full textZednik, 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.
Full textMazzola, Guerino, Maria Mannone, Yan Pang, Margaret O’Brien, and Nathan Torunsky. "Neuroscience and Gestures." In Computational Music Science, 155–61. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47334-5_18.
Full textVenugopal, Sharmila, Sharon Crook, Malathi Srivatsan, and Ranu Jung. "Principles of Computational Neuroscience." In Biohybrid Systems, 11–30. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527639366.ch2.
Full textIrvine, Liz. "Simulation in computational neuroscience." In The Routledge Handbook of the Computational Mind, 370–80. Milton Park, Abingdon, Oxon ; New York : Routledge, 2019. |: Routledge, 2018. http://dx.doi.org/10.4324/9781315643670-28.
Full textEasttom, Chuck. "Introduction to Computational Neuroscience." In Machine Learning for Neuroscience, 147–71. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003230588-10.
Full textDeWan, Andrew, Lana C. Rutherford, and Gina G. Turrigiano. "Activity-Dependent Regulation of Inhibition in Visual Cortical Cultures." In Computational Neuroscience, 3–6. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-9800-5_1.
Full textChitwood, Raymond A., Brenda J. Claiborne, and David B. Jaffe. "Modeling the Passive Properties of Nonpyramidal Neurons in Hippocampal Area CA3." In Computational Neuroscience, 59–64. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-9800-5_10.
Full textConference papers on the topic "Computational neuroscience"
Maley, 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.
Full textPiccinini, Gualtiero. "Non-Computational Functionalism: Computation and the Function of Consciousness." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1022-0.
Full textKawato, Mitsuo. "Computational Neuroscience and Multiple-Valued Logic." In 2009 39th International Symposium on Multiple-Valued Logic. IEEE, 2009. http://dx.doi.org/10.1109/ismvl.2009.70.
Full textTirupattur, Naveen, Christopher C. Lapish, Snehasis Mukhopadhyay, Tuan D. Pham, Xiaobo Zhou, Hiroshi Tanaka, Mayumi Oyama-Higa, et al. "Text Mining for Neuroscience." In 2011 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MODELS FOR LIFE SCIENCES (CMLS-11). AIP, 2011. http://dx.doi.org/10.1063/1.3596634.
Full textGao, Richard, Dylan Christiano, Tom Donoghue, and Bradley Voytek. "The Structure of Cognition Across Computational Cognitive Neuroscience." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1426-0.
Full textJosé Macário Costa, Raimundo, Luís Alfredo Vidal de Carvalho, Emilio Sánchez Miguel, Renata Mousinho, Renato Cerceau, Lizete Pontes Macário Costa, Jorge Zavaleta, Laci Mary Barbosa Manhães, and Sérgio Manuel Serra da Cruz. "Computational Neuroscience - Challenges and Implications for Brazilian Education." In 7th International Conference on Computer Supported Education. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005481004360441.
Full textChateau-Laurent, Hugo, and Frederic Alexandre. "Towards a Computational Cognitive Neuroscience Model of Creativity." In 2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2021. http://dx.doi.org/10.1109/iccicc53683.2021.9811309.
Full textZhang, Wen-Ran. "Six Conjectures in Quantum Physics and Computational Neuroscience." In 2009 Third International Conference on Quantum, Nano and Micro Technologies (ICQNM). IEEE, 2009. http://dx.doi.org/10.1109/icqnm.2009.32.
Full textAnllo, Hernan, Gil Salamander, Stefano Palminteri, Nichola Raihani, and Uri Hertz. "Computational drivers of advice-giving." In 2023 Conference on Cognitive Computational Neuroscience. Oxford, United Kingdom: Cognitive Computational Neuroscience, 2023. http://dx.doi.org/10.32470/ccn.2023.1367-0.
Full textMuzellec, Sabine, Mathieu Chalvidal, Thomas Serre, and Rufin VanRullen. "Accurate implementation of computational neuroscience models through neural ODEs." In 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1165-0.
Full textReports on the topic "Computational neuroscience"
Bower, James M., and Christof Koch. Methods in Computational Neuroscience. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada231397.
Full textBower, James M., and Christof Koch. Training in Methods in Computational Neuroscience. Fort Belvoir, VA: Defense Technical Information Center, August 1992. http://dx.doi.org/10.21236/ada261806.
Full textHalvorson, Harlyn O. Training in Methods in Computational Neuroscience. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada217018.
Full textBower, James M., and Christof Koch. Methods in Computational Neuroscience: Marine Biology Laboratory Student Projects. Fort Belvoir, VA: Defense Technical Information Center, November 1988. http://dx.doi.org/10.21236/ada201434.
Full textSchunn, 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.
Full textSejonowski, T. Workshop in Computational Neuroscience (8th) held in Woods Hole, Massachusetts on 22-28 August 1992. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada279786.
Full textSemerikov, Serhiy O., Illia O. Teplytskyi, Yuliia V. Yechkalo, and Arnold E. Kiv. Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2648.
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