Relevant bibliographies by topics / Computational neuroscience

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Computational neuroscience'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Computational neuroscience"

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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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Becker, 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.

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Dissertations / Theses on the topic "Computational 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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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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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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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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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.

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Sparse coding is an influential unsupervised learning approach proposed as a theoretical model of the encoding process in the primary visual cortex (V1). While sparse coding has been successful in explaining classical receptive field properties of simple cells, it was unclear whether it can account for more complex response properties in a variety of cell types. In this dissertation, we demonstrate that sparse coding and its variants are consistent with key aspects of neural response in V1, including many contextual and nonlinear effects, a number of inhibitory interneuron properties, as wel
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Woldman, 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.

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In this thesis mathematical techniques and models are applied to electroencephalographic (EEG) recordings to study mechanisms of idiopathic generalised epilepsy (IGE). First, we compare network structures derived from resting-state EEG from people with IGE, their unaffected relatives, and healthy controls. Next, these static networks are combined with a dynamical model describing the ac- tivity of a cortical region as a population of phase-oscillators. We then examine the potential of the differences found in the static networks and the emergent properties of the dynamic network as individual
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Nguyen, 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.

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Recent advances of artificial neural networks and deep learning model have produced significant results in problems related to neuroscience. For example, deep learning models have demonstrated superior performance in non-linear, multivariate pattern classification problems such as Alzheimer’s disease classification, brain lesion segmentation, skull stripping and brain age prediction. Deep learning provides unique advantages for high-dimensional data such as MRI data, since it does not require extensive feature engineering. The thesis investigates three problems related to neuroscience and disc
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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.

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Books on the topic "Computational 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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Book chapters on the topic "Computational 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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Mazzola, Guerino, Maria Mannone, Yan Pang, Margaret O’Brien, and Nathan Torunsky. "Neuroscience and Gestures." In Computational Music Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47334-5_18.

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DeWan, Andrew, Lana C. Rutherford, and Gina G. Turrigiano. "Activity-Dependent Regulation of Inhibition in Visual Cortical Cultures." In Computational Neuroscience. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-9800-5_1.

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Chitwood, Raymond A., Brenda J. Claiborne, and David B. Jaffe. "Modeling the Passive Properties of Nonpyramidal Neurons in Hippocampal Area CA3." In Computational Neuroscience. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-9800-5_10.

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Conference papers on the topic "Computational neuroscience"

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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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Piccinini, Gualtiero. "Non-Computational Functionalism: Computation and the Function of Consciousness." In 2018 Conference on Cognitive Computational Neuroscience. Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1022-0.

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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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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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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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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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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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Anllo, Hernan, Gil Salamander, Stefano Palminteri, Nichola Raihani, and Uri Hertz. "Computational drivers of advice-giving." In 2023 Conference on Cognitive Computational Neuroscience. Cognitive Computational Neuroscience, 2023. http://dx.doi.org/10.32470/ccn.2023.1367-0.

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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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Reports on the topic "Computational 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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