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M., Namura K. (2003). GRAPE-6: Massively-Parallel Special-Purpose Computer for Astrophysical Particle Simulations / Publications of the Astronomical Society of Japan, Vol. 55, Iss. 6, 25 December 2003, pp. 1163—1187 // https://doi.org/10.1093/pasj/55.6.1163

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The Board: Neurogrid (2009) / Stanford University: Brains in Silicon // https://web.stanford.edu/group/brainsinsilicon/neurogrid.html

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Benjamin B. V., Gao P., McQuinn E., Choudhary S., Chandrasekaran A. R., Bussat J., Alvarez-Icaza R., Arthur J. V., Merolla P. A., Boahen K. (2014). Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations / Proceedings of the IEEE, Vol. 102, No. 5, May 2014 // https://doi.org/10.1109/JPROC.2014.2313565

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Davison A. P., Müller E., Schmitt S., Vogginger B., Lester D., Pfeil T. (2020). HBP Neuromorphic Computing Platform Guidebook. Release 2020-01-21 09:32:46 (cc9c98a) / Human Brain Project — Neuromorphic Computing Platform // https://flagship.kip.uni-heidelberg.de/jss/FileExchange/HBPNeuromorphicComputingPlatformGuidebook.pdf?fID=1504&s=qqdXDg6HuX3&uID=65

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Schmitt S., Klähn J., Bellec G., Grüb A., Güttler M., Hartel A., Hartmann S., Husmann D., Husmann K., Jeltsch S., Karasenko V., Kleider M., Koke C., Kononov A., Mauch C., Müller E., Müller P., Partzsch J., Petrovici M. A., Schiefer S., Scholze S., Thanasoulis V., Vogginger B., Legenstein R., Maass W., Mayr C., Schüffny R., Schemmel J., Meier K. (2017). Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System / 2017 International Joint Conference on Neural Network // https://doi.rog/10.1109/IJCNN.2017.7966125

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Hardware (2020) / Human Brain Project // https://www.humanbrainproject.eu/en/silicon-brains/how-we-work/hardware/

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Schemmel J., Kriener L., Müller P., Meier K. (2017). An Accelerated Analog Neuromorphic Hardware System Emulating NMDA- and Calcium-Based Non-Linear Dendrites // 2017 International Joint Conference on Neural Networks / https://doi.org/10.1109/IJCNN.2017.7966124

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Schmitt S., Müller E. (2019). BrainScaleS Hands-On Tutorial: Overview / NICE Workshop, 2019 // https://niceworkshop.org/wp-content/uploads/2019/04/NICE-2019-Day-4a_BrainScaleS-Overview.pdf

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Hardware (2020) / Human Brain Project // https://www.humanbrainproject.eu/en/silicon-brains/how-we-work/hardware/

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Illing D., Gerstner W., Brea J. (2019). Biologically plausible deep learning — But how far can we go with shallow networks? / Neural Networks, Vol. 118, pp. 90—101 // https://doi.org/10.1016/j.neunet.2019.06.001

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Bliss T. V. P. (1979). Synaptic plasticity in the hippocampus / Trends in Neurosciences, Vol. 2, pp. 42—45 // https://doi.org/10.1016/0166-2236(79)90019-5

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Kandel E. R., Tauc L. (1965). Heterosynaptic facilitation in neurones of the abdominal ganglion of Aplysia depilans / The Journal of Physiology, Vol. 181, Iss. 1, pp. 1—27 // https://doi.org/10.1113/jphysiol.1965.sp007742

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Taylor M. M. (1973). The Problem of Stimulus Structure in the Behavioural Theory of Perception / South African Journal of Psychology, Vol. 3, pp. 23—45 // https://www.researchgate.net/publication/298214719_The_Problem_of_Stimulus_Structure_in_the_Behavioural_Theory_of_Perception

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Bliss T. V., Lomo T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path / The Journal Of Physiology, Vol. 232, No. 2, pp. 331—356 // https://doi.org/10.1113/jphysiol.1973.sp010273

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Rall W., Rinzel J. (1971). Dendritic spine function and synaptic attenuation calculations / Program and Abstracts: Society for Neuroscience First annual meeting, p. 64

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Zemlyanukhin A. I., Bochkarev A. V. (2019). Analytical Properties and Solutions of the FitzHugh—Rinzel Model / Russian Journal of Nonlinear Dynamics, 2019, vol. 15, no. 1, pp. 3–12 // https://doi.org/10.20537/nd190101

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Rahimian E., Zabihi S., Amiri M., Linares-Barranco B. (2017). Digital Implementation of the Two-Compartmental Pinsky-Rinzel Pyramidal Neuron Model / IEEE Transactions on Biomedical Circuits and Systems, 2018-Feb; 12(1):47-57 // https://doi.org/10.1109/TBCAS.2017.2753541

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Lynch M. A. (2004). Long-term potentiation and memory / Physiological Reviews, Vol. 84, Iss. 1, pp. 87—136 // https://doi.org/10.1152/physrev.00014.2003

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Segal M., Murphy D. D. (1999). CREB activation mediates plasticity in cultured hippocampal neurons / Neural Plasticity, Vol. 6, Iss. 3, pp. 1—7 // https://doi.org/10.1155/NP.1998.1

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Emptage N. J., Reid C. A., Fine A., Bliss T. V. (2003). Optical quantal analysis reveals a presynaptic component of LTP at hippocampal Schaffer-associational synapses / Neuron, Vol. 38, Iss. 5, pp. 797—804 // https://doi.org/10.1016/S0896-6273(03)00325-8

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Tang Y. P., Shimizu E., Dube G. R., Rampon C., Kerchner G. A., Zhuo M., Liu G., Tsien J. Z. (1999). Genetic enhancement of learning and memory in mice / Nature, Vol. 401 (6748), pp. 63—69 // https://doi.org/10.1038/43432

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Tang Y., Wang H., Feng R., Kyin M., Tsien J. (2001). Differential effects of enrichment on learning and memory function in NR2B transgenic mice / Neuropharmacology, Vol. 41, Iss. 6, pp. 779—790 // https://doi.org/10.1016/S0028-3908(01)00122-8

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Malinow R. (2003). AMPA receptor trafficking and long-term potentiation / Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, Vol. 358 (1432), pp. 707—14 // https://doi.org/10.1098/rstb.2002.1233

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Malenka R. C., Bear M. F. (2004). LTP and LTD: an embarrassment of riches / Neuron, Vol. 44, Iss. 1, pp. 5—21 // https://doi.oeg/10.1016/j.neuron.2004.09.012

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Araya R., Vogels T. P., Yuste R. (2014). Activity-dependent dendritic spine neck changes are correlated with synaptic strength // PNAS, Vol. 111, Iss. 28, pp. E2895—E2904 // https://doi.org/10.1073/pnas.1321869111

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Douglas R. M., Goddard G. V. (1975). Long-term potentiation of the perforant path-granule cell synapse in the rat hippocampus / Brain Research, Vol. 86, Iss. 2, 21-Mar-1975, pp. 205—215 // https://doi.org/10.1016/0006-8993(75)90697-6

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