Bibliografie tematiche / Computationnal Neuroscience

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Letteratura scientifica selezionata sul tema "Computationnal Neuroscience"

Autore: Grafiati
Pubblicato: 7 luglio 2024
Ultima modifica: 31 luglio 2025

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Articoli di riviste sul tema "Computationnal Neuroscience"

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Herrmann-Pillath, Carsten. "From dual systems to dual function: rethinking methodological foundations of behavioural economics." Economics and Philosophy 35, no. 3 (2019): 403–22. http://dx.doi.org/10.1017/s0266267118000378.

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AbstractBuilding on an overview of dual systems theories in behavioural economics, the paper presents a methodological assessment in terms of the mechanistic explanations framework that has gained prominence in philosophy of the neurosciences. I conclude that they fail to meet the standards of causal explanations and I suggest an alternative ‘dual functions’ view based on Marr’s methodology of computational neuroscience. Recent psychological and neuroscience research undermines the case for a categorization of brain processes in terms of properties such as relative speed. I defend an interpret
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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.

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Sejnowski, T., C. Koch, and P. Churchland. "Computational neuroscience." Science 241, no. 4871 (1988): 1299–306. http://dx.doi.org/10.1126/science.3045969.

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Sejnowski, Terrence J. "Computational neuroscience." Behavioral and Brain Sciences 9, no. 1 (1986): 104–5. http://dx.doi.org/10.1017/s0140525x00021713.

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Moore, John W. "Computational Neuroscience." Contemporary Psychology: A Journal of Reviews 38, no. 2 (1993): 137–39. http://dx.doi.org/10.1037/033019.

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Ringo, J. L. "Computational Neuroscience." Archives of Neurology 48, no. 2 (1991): 130. http://dx.doi.org/10.1001/archneur.1991.00530140018008.

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Kriegeskorte, Nikolaus, and Pamela K. Douglas. "Cognitive computational neuroscience." Nature Neuroscience 21, no. 9 (2018): 1148–60. http://dx.doi.org/10.1038/s41593-018-0210-5.

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Cecchi, Guillermo A., and James Kozloski. "Preface: Computational neuroscience." IBM Journal of Research and Development 61, no. 2/3 (2017): 0:1–0:4. http://dx.doi.org/10.1147/jrd.2017.2690118.

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Popovych, Oleksandr, Peter Tass, and Christian Hauptmann. "Desynchronization (computational neuroscience)." Scholarpedia 6, no. 10 (2011): 1352. http://dx.doi.org/10.4249/scholarpedia.1352.

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Érdi, Péter. "Teaching computational neuroscience." Cognitive Neurodynamics 9, no. 5 (2015): 479–85. http://dx.doi.org/10.1007/s11571-015-9340-6.

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Tesi sul tema "Computationnal Neuroscience"

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Higgins, Irina. "Computational neuroscience of speech recognition." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:daa8d096-6534-4174-b63e-cc4161291c90.

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Physical variability of speech combined with its perceptual constancy make speech recognition a challenging task. The human auditory brain, however, is able to perform speech recognition effortlessly. This thesis aims to understand the precise computational mechanisms that allow the auditory brain to do so. In particular, we look for the minimal subset of sub-cortical auditory brain areas that allow the primary auditory cortex to learn 'good representations' of speech-like auditory objects through spike-timing dependent plasticity (STDP) learning mechanisms as described by Bi & Poo (1998). A '
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Walters, 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.

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Head direction cells signal the orientation of the head in the horizontal plane. This thesis shows how some of the known head direction cell response properties might develop through learning. The research methodology employed is the computer simulation of neural network models of head direction cells that self-organize through learning. The preferred firing directions of head direction cells will change in response to the manipulation of distal visual cues, but not in response to the manipulation of proximal visual cues. Simulation results are presented of neural network models that learn to
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Chateau-Laurent, Hugo. "Modélisation Computationnelle des Interactions Entre Mémoire Épisodique et Contrôle Cognitif." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0019.

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La mémoire épisodique est souvent illustrée par la madeleine de Proust comme la capacité à revivre une situation du passé suite à la perception d'un stimulus. Ce scénario simpliste ne doit pas mener à penser que la mémoire opère en isolation des autres fonctions cognitives. Au contraire, la mémoire traite des informations hautement transformées et est elle-même modulée par les fonctions exécutives pour informer la prise de décision. Ces interactions complexes donnent lieu à des fonctions cognitives supérieures comme la capacité à imaginer de futures séquences d'événements potentielles en combi
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Cronin, Beau D. "Quantifying uncertainty in computational neuroscience with Bayesian statistical inference." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45336.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2008.<br>Includes bibliographical references (p. 101-106).<br>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 e
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Lee, Ray A. "Analysis of Spreading Depolarization as a Traveling Wave in a Neuron-Astrocyte Network." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503308416771087.

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Allen, John Michael. "Effects of Abstraction and Assumptions on Modeling Motoneuron Pool Output." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1495538117787703.

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Shepardson, Dylan. "Algorithms for inverting Hodgkin-Huxley type neuron models." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31686.

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Thesis (Ph.D)--Algorithms, Combinatorics, and Optimization, Georgia Institute of Technology, 2010.<br>Committee 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.
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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.

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Tromans, 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.

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Abstract (sommario):
Neural responses in the primate ventral visual system become more complex in the later stages of the pathway. For example, not only do neurons in IT cortex respond to complete objects, they also learn to respond invariantly with respect to the viewing angle of an object and also with respect to the location of an object. These types of neural responses have helped guide past research with VisNet, a computational model of the primate ventral visual pathway that self-organises during learning. In particular, previous research has focussed on presenting to the model one object at a time during tr
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Vellmer, 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.

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In dieser Arbeit werden mithilfe der Fokker-Planck-Gleichung die Statistiken, vor allem die Leistungsspektren, von Punktprozessen berechnet, die von mehrdimensionalen Integratorneuronen [Engl. integrate-and-fire (IF) neuron], Netzwerken von IF Neuronen und Entscheidungsfindungsmodellen erzeugt werden. Im Gehirn werden Informationen durch Pulszüge von Aktionspotentialen kodiert. IF Neurone mit radikal vereinfachter Erzeugung von Aktionspotentialen haben sich in Studien die auf Pulszeiten fokussiert sind als Standardmodelle etabliert. Eindimensionale IF Modelle können jedoch beobachtetes Pul
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Libri sul tema "Computationnal Neuroscience"

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Ribeiro, Paulo Rogério de Almeida, Vinícius Rosa Cota, Dante Augusto Couto Barone, and Alexandre César Muniz de Oliveira, eds. Computational Neuroscience. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08443-0.

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Cota, Vinícius Rosa, Dante Augusto Couto Barone, Diego Roberto Colombo Dias, and Laila Cristina Moreira Damázio, eds. Computational Neuroscience. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36636-0.

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Bower, James M., ed. Computational Neuroscience. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-9800-5.

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Chaovalitwongse, Wanpracha, Panos M. Pardalos, and Petros Xanthopoulos, eds. Computational Neuroscience. Springer New York, 2010. http://dx.doi.org/10.1007/978-0-387-88630-5.

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Barone, Dante Augusto Couto, Eduardo Oliveira Teles, and Christian Puhlmann Brackmann, eds. Computational Neuroscience. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71011-2.

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Mallot, Hanspeter A. Computational Neuroscience. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00861-5.

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Bower, James M., ed. Computational Neuroscience. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-4831-7.

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Stoyanov, Drozdstoy, Bogdan Draganski, Paolo Brambilla, and Claus Lamm, eds. Computational Neuroscience. Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3230-7.

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Riascos Salas, Jaime A., Vinícius Rosa Cota, Hernán Villota, and Daniel Betancur Vasquez, eds. Computational Neuroscience. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63848-0.

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Pardalos, P. M. Computational neuroscience. Springer, 2010.

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Capitoli di libri sul tema "Computationnal Neuroscience"

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Asif, Mohammad, Princy Choudhary, and Anushka Ajit Dandawate. "Computational Neuroscience." In Synaptic Plasticity in Neurodegenerative Disorders. CRC Press, 2024. http://dx.doi.org/10.1201/9781003464648-10.

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Choudhary, Princy, Mohammad Asif, and Diya Srivastava. "Computational Neuroscience." In Synaptic Plasticity in Neurodegenerative Disorders. CRC Press, 2024. http://dx.doi.org/10.1201/9781003464648-13.

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Hasselmo, Michael E., and James R. Hinman. "Computational Neuroscience: Hippocampus." In Neuroscience in the 21st Century. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3474-4_175.

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Hasselmo, Michael E., and James R. Hinman. "Computational Neuroscience: Hippocampus." In Neuroscience in the 21st Century. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4614-6434-1_175-1.

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Hasselmo, Michael E., and James R. Hinman. "Computational Neuroscience: Hippocampus." In Neuroscience in the 21st Century. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88832-9_175.

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Zednik, Carlos. "Computational cognitive neuroscience." In The Routledge Handbook of the Computational Mind. Routledge, 2018. http://dx.doi.org/10.4324/9781315643670-27.

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Wang, Xiao-Jing. "Computational Psychiatry." In Theoretical Neuroscience. CRC Press, 2025. https://doi.org/10.1201/9781003459361-13.

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Venugopal, Sharmila, Sharon Crook, Malathi Srivatsan, and Ranu Jung. "Principles of Computational Neuroscience." In Biohybrid Systems. Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527639366.ch2.

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Easttom, Chuck. "Introduction to Computational Neuroscience." In Machine Learning for Neuroscience. CRC Press, 2023. http://dx.doi.org/10.1201/9781003230588-10.

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Irvine, Liz. "Simulation in computational neuroscience." In The Routledge Handbook of the Computational Mind. Routledge, 2018. http://dx.doi.org/10.4324/9781315643670-28.

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Atti di convegni sul tema "Computationnal Neuroscience"

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Sobhani, Vida, Jan Lorenz, and Tobias Gemmeke. "Inter-node Communication in Multi-FPGA-based Computational Neuroscience Simulators: PHY-to-Link." In 2024 IEEE Nordic Circuits and Systems Conference (NorCAS). IEEE, 2024. http://dx.doi.org/10.1109/norcas64408.2024.10752479.

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Kawato, 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.

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Maley, Corey. "Analog Computation in Computational Cognitive Neuroscience." In 2018 Conference on Cognitive Computational Neuroscience. Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1178-0.

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José Macário Costa, Raimundo, Luís Alfredo Vidal de Carvalho, Emilio Sánchez Miguel, et al. "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.

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Gao, Richard, Dylan Christiano, Tom Donoghue, and Bradley Voytek. "The Structure of Cognition Across Computational Cognitive Neuroscience." In 2019 Conference on Cognitive Computational Neuroscience. Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1426-0.

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Chateau-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.

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Zhang, 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.

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Tirupattur, Naveen, Christopher C. Lapish, Snehasis Mukhopadhyay, 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.

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Karimimehr, Saeed. "A novel face recognition system inspired by computational neuroscience." In IEEE EUROCON 2013. IEEE, 2013. http://dx.doi.org/10.1109/eurocon.2013.6731009.

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Muzellec, Sabine, Mathieu Chalvidal, Thomas Serre, and Rufin VanRullen. "Accurate implementation of computational neuroscience models through neural ODEs." In 2022 Conference on Cognitive Computational Neuroscience. Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1165-0.

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Rapporti di organizzazioni sul tema "Computationnal Neuroscience"

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Bower, James M., and Christof Koch. Methods in Computational Neuroscience. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada231397.

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Bower, James M., and Christof Koch. Training in Methods in Computational Neuroscience. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada261806.

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Halvorson, Harlyn O. Training in Methods in Computational Neuroscience. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada217018.

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Bower, James M., and Christof Koch. Methods in Computational Neuroscience: Marine Biology Laboratory Student Projects. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada201434.

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Schunn, C. D. A Review of Human Spatial Representations Computational, Neuroscience, Mathematical, Developmental, and Cognitive Psychology Considerations. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada440864.

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Sejonowski, T. Workshop in Computational Neuroscience (8th) held in Woods Hole, Massachusetts on 22-28 August 1992. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada279786.

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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capa
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Semerikov, 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.], 2018. http://dx.doi.org/10.31812/123456789/2648.

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The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet ad
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