Relevant bibliographies by topics / In silico neurons and synapses

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'In silico neurons and synapses'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "In silico neurons and synapses"

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Kanazawa, Yusuke, Tetsuya Asai, and Yoshihito Amemiya. "Basic Circuit Design of a Neural Processor: Analog CMOS Implementation of Spiking Neurons and Dynamic Synapses." Journal of Robotics and Mechatronics 15, no. 2 (2003): 208–18. http://dx.doi.org/10.20965/jrm.2003.p0208.

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We discuss the integration architecture of spiking neurons, predicted to be next-generation basic circuits of neural processor and dynamic synapse circuits. A key to development of a brain-like processor is to learn from the brain. Learning from the brain, we try to develop circuits implementing neuron and synapse functions while enabling large-scale integration, so large-scale integrated circuits (LSIs) realize functional behavior of neural networks. With such VLSI, we try to construct a large-scale neural network on a single semiconductor chip. With circuit integration now reaching micron le
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Hjorth, J. J. Johannes, Alexander Kozlov, Ilaria Carannante, et al. "The microcircuits of striatum in silico." Proceedings of the National Academy of Sciences 117, no. 17 (2020): 9554–65. http://dx.doi.org/10.1073/pnas.2000671117.

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The basal ganglia play an important role in decision making and selection of action primarily based on input from cortex, thalamus, and the dopamine system. Their main input structure, striatum, is central to this process. It consists of two types of projection neurons, together representing 95% of the neurons, and 5% of interneurons, among which are the cholinergic, fast-spiking, and low threshold-spiking subtypes. The membrane properties, soma–dendritic shape, and intrastriatal and extrastriatal synaptic interactions of these neurons are quite well described in the mouse, and therefore they
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Vogelstein, R. Jacob, Udayan Mallik, Eugenio Culurciello, Gert Cauwenberghs, and Ralph Etienne-Cummings. "A Multichip Neuromorphic System for Spike-Based Visual Information Processing." Neural Computation 19, no. 9 (2007): 2281–300. http://dx.doi.org/10.1162/neco.2007.19.9.2281.

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We present a multichip, mixed-signal VLSI system for spike-based vision processing. The system consists of an 80 × 60 pixel neuromorphic retina and a 4800 neuron silicon cortex with 4,194,304 synapses. Its functionality is illustrated with experimental data on multiple components of an attention-based hierarchical model of cortical object recognition, including feature coding, salience detection, and foveation. This model exploits arbitrary and reconfigurable connectivity between cells in the multichip architecture, achieved by asynchronously routing neural spike events within and between chip
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Boegerhausen, Malte, Pascal Suter, and Shih-Chii Liu. "Modeling Short-Term Synaptic Depression in Silicon." Neural Computation 15, no. 2 (2003): 331–48. http://dx.doi.org/10.1162/089976603762552942.

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We describe a model of short-term synaptic depression that is derived from a circuit implementation. The dynamics of this circuit model is similar to the dynamics of some theoretical models of short-term depression except that the recovery dynamics of the variable describing the depression is nonlinear and it also depends on the presynaptic frequency. The equations describing the steady-state and transient responses of this synaptic model are compared to the experimental results obtained from a fabricated silicon network consisting of leaky integrate-and-fire neurons and different types of sho
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Bofill-i-Petit, A., and A. F. Murray. "Synchrony Detection and Amplification by Silicon Neurons With STDP Synapses." IEEE Transactions on Neural Networks 15, no. 5 (2004): 1296–304. http://dx.doi.org/10.1109/tnn.2004.832842.

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Vogelstein, R. Jacob, Udayan Mallik, Joshua T. Vogelstein, and Gert Cauwenberghs. "Dynamically Reconfigurable Silicon Array of Spiking Neurons With Conductance-Based Synapses." IEEE Transactions on Neural Networks 18, no. 1 (2007): 253–65. http://dx.doi.org/10.1109/tnn.2006.883007.

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Covi, E., R. George, J. Frascaroli, et al. "Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons." Journal of Physics D: Applied Physics 51, no. 34 (2018): 344003. http://dx.doi.org/10.1088/1361-6463/aad361.

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Sueviriyapan, Natthapong, Chak Foon Tso, Erik D. Herzog, and Michael A. Henson. "Astrocytic Modulation of Neuronal Activity in the Suprachiasmatic Nucleus: Insights from Mathematical Modeling." Journal of Biological Rhythms 35, no. 3 (2020): 287–301. http://dx.doi.org/10.1177/0748730420913672.

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The suprachiasmatic nucleus (SCN) of the hypothalamus consists of a highly heterogeneous neuronal population networked together to allow precise and robust circadian timekeeping in mammals. While the critical importance of SCN neurons in regulating circadian rhythms has been extensively studied, the roles of SCN astrocytes in circadian system function are not well understood. Recent experiments have demonstrated that SCN astrocytes are circadian oscillators with the same functional clock genes as SCN neurons. Astrocytes generate rhythmic outputs that are thought to modulate neuronal activity t
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Jenkner, Martin, Bernt Müller, and Peter Fromherz. "Interfacing a silicon chip to pairs of snail neurons connected by electrical synapses." Biological Cybernetics 84, no. 4 (2001): 239–49. http://dx.doi.org/10.1007/s004220000218.

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Rasch, Malte J., Klaus Schuch, Nikos K. Logothetis, and Wolfgang Maass. "Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1." Journal of Neurophysiology 105, no. 2 (2011): 757–78. http://dx.doi.org/10.1152/jn.00845.2009.

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A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characteriza
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Dissertations / Theses on the topic "In silico neurons and synapses"

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Vissani, Matteo. "Multisensory features of peripersonal space representation: an analysis via neural network modelling." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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The peripersonal space (PPS) is the space immediately surrounding the body. It is coded in the brain in a multisensory, body part-centered (e.g. hand-centered, trunk-centered), modular fashion. This is supported by the existence of multisensory neurons (in fronto-parietal areas) with tactile receptive field on a specific body part (hand, arm, trunk, etc.) and visual/auditory receptive field surrounding the same body part. Recent behavioural results (Serino et al. Sci Rep 2015), obtained by using an audio-tactile paradigm, have further supported the existence of distinct PPS representations, ea
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Boussa, Sofiane. "Réseaux hybrides in silico/in vitro par connexion dynamique entre cellule excitable et modèles numériques." Le Havre, 2009. http://www.theses.fr/2009LEHA0002.

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Au sein du système nerveux, les neurones, massivement interconnectés, planifient les commandes motrices, régulent les sécrétions hormonales, assurent l'homéostasie du milieu intérieur, maintiennent les fonctions végétatives et supportent les mécanismes cognitifs supérieurs. Les fonctions neuronales reposent à la fois sur la topologie des projections nerveuses et sur des processus intégratifs et adaptatifs propres à chaque cellule comme la plasticité synaptique ou l'apprentissage par coïncidence pré/post. Dans le domaine de l’automatique les réseaux neuronaux artificiels, sont des systèmes adap
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Merz, David C. (David Christian). "Synapse formation between identified leech neurons." Thesis, McGill University, 1994. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=28848.

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The formation of patterns of functionally appropriate chemical synapses is one of the key aspects of nervous system development. I have investigated the cellular interactions that culminate in the formation of an inhibitory synapse between the R and P neurons of the leech. These neurons may be isolated and maintained in culture, where they reform synaptic connections under easily manipulable conditions. An early event in the formation of this synapse is the loss from sites of contact in the postsynaptic P cell of an excitatory response to the transmitter serotonin. The loss of this response wa
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Ching, Shim. "Synaptogenesis between identified neurons." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=55449.

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Serotonergic Retzius (R) neurons of the leech Hirudo medicinalis in culture reform inhibitory synapses with pressure sensitive (P) neurons while selectively reducing an extrasynaptic, depolarizing response to serotonin (5-HT) in the P neuron. We have examined if the selection of 5-HT responses is restricted to sites of contact between processes and growth cones of these cells. As measured by intracellular recording at the soma, focal application of 5-HT depolarized uncontacted P cell bodies, neurites and growth cones but not processes contacted by R cells. In patch clamp recordings of the depo
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Reese, David R. "Neuropilar synaptogenesis between identified central neurons in vivo." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0007/MQ44256.pdf.

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Meuth, Patrick [Verfasser], and Martin [Akademischer Betreuer] Burger. "Thalamic neurons in silico / Patrick Meuth. Betreuer: Martin Burger." Münster : Universitäts- und Landesbibliothek der Westfälischen Wilhelms-Universität, 2011. http://d-nb.info/1027017827/34.

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Javalet, Charlotte. "Rôle des exosomes comme nouvelle voie de communication entre les neurones." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAV028/document.

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Les exosomes sont des vésicules d’origine endosomale sécrétées par les cellules dans leur environnement après fusion à la membrane plasmique des endosomes multivésiculés. Les exosomes représentent un nouveau mode de communication entre les cellules en permettant un transfert direct de protéines, de lipides et d’ARN. L’objectif de ma thèse était d’étudier le rôle des exosomes dans la communication entre les neurones. Précédemment, le laboratoire a montré que les neurones sécrètent des exosomes de manière régulée par l’activité synaptique. Nous avons observé que les exosomes neuronaux ne sont en
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Pallotto, Marta. "GABAergic signaling and synaptic integration of adult-generated neurons." Paris 6, 2012. http://www.theses.fr/2012PA066677.

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La neurogenèse adulte représente une forme unique de plasticité neuronale, qui, chez les mammifères, est limitée à l’hippocampe et au système olfactif. Dans le bulbe olfactif (BO) les neurones s’intègrent sans cesse aux réseaux préexistants et se différentient. La majorité de ces cellules forment une population d’interneurones GABAergique : Les cellules granulaires (GCs). Dans ce travail, j’ai étudié l’intégration synaptique des GCs nouvellement générées dans le BO de souris adultes. Afin de visualiser ces cellules, j’ai injecté un vecteur viral codant pour l’eGFP dans le courant de migration
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Banks, Glen B. "The role of synapse formation on motoneuron survival during embryonic development /." [St. Lucia, Qld.], 2003. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe17453.pdf.

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Hetsch, Florian Jan Alexander [Verfasser]. "Induction of Synapses by Agrin in Cultured Cortical Neurons / Florian Jan Alexander Hetsch." Berlin : Freie Universität Berlin, 2015. http://d-nb.info/1072622262/34.

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Books on the topic "In silico neurons and synapses"

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Haycock, R. J. Hardware neurons and synapses for pulse stream neural networks. UMIST, 1997.

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Masashi, Inoue, ed. Nyūron no seibutsu butsuri. 2nd ed. Maruzen Shuppan, 2013.

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Proteins, transmitters, and synapses. Blackwell Scientific Publications, 1994.

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Parvin, Manoucher. Out of the gray: A concerto for neurons and synapses, a novel. Ibex Publishers, 2011.

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1933-, Shepherd Gordon M., Black Ira B, and Killackey Herbert P, eds. Synapses, circuits, and the beginnings of memory. MIT Press, 1986.

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Shepherd, Gordon M. The Synaptic Organization of the Brain. Oxford University Press, 1990.

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1949-, Schüz A., ed. Anatomy of the cortex: Statistics and geometry. Springer-Verlag, 1991.

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van, Pelt J., ed. Development, dynamics, and pathology of neuronal networks: From molecules to functional circuits : proceedings of the 23rd International Summer School of Brain Research, held at the Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands, from 25-29 August 2003. Elsevier, 2005.

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Braitenberg, Valentino. Cortex: Statistics and geometry of neuronal connectivity. 2nd ed. Springer, 1998.

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Christof, Koch, and Segev Idan, eds. Methods in neuronal modeling: From synapses to networks. MIT Press, 1992.

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Book chapters on the topic "In silico neurons and synapses"

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Lazzaro, John, and John Wawrzynek. "Low-Power Silicon Neurons, Axons and Synapses." In Silicon Implementation of Pulse Coded Neural Networks. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2680-3_8.

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Watts, Lloyd. "Designing Networks of Spiking Silicon Neurons and Synapses." In Computation and Neural Systems. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3254-5_20.

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Lytton, William W., and Cliff C. Kerr. "Computational Neuroscience of Synapses and Neurons." In Neuroscience in the 21st Century. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-1997-6_86.

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Lytton, William W., and Cliff C. Kerr. "Computational Neuroscience of Synapses and Neurons." In Neuroscience in the 21st Century. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3474-4_86.

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Lee, Bang W., and Bing J. Sheu. "Programmable Synapses and Gain-Adjustable Neurons." In Hardware Annealing in Analog VLSI Neurocomputing. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3984-1_4.

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Giacobini, Ezio. "Development of Peripheral Parasympathetic Neurons and Synapses." In Developmental Neurobiology of the Autonomic Nervous System. Humana Press, 1986. http://dx.doi.org/10.1007/978-1-59259-459-7_2.

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Ramacher, Ulrich, and Christoph von der Malsburg. "Elementary Circuits for Neurons, Synapses, and Photosensors." In On the Construction of Artificial Brains. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-00189-5_13.

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Bolam, J. P. "Synapses of Identified Neurons in the Neostriatum." In Ciba Foundation Symposium 107 - Functions of the Basal Ganglia. John Wiley & Sons, Ltd., 2008. http://dx.doi.org/10.1002/9780470720882.ch3.

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Peters, Alan. "Number of Neurons and Synapses in Primary Visual Cortex." In Cerebral Cortex. Springer US, 1987. http://dx.doi.org/10.1007/978-1-4615-6616-8_7.

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Braitenberg, Valentino, and Almut Schüz. "Comparison Between the Densities of Neurons, Synapses and Axons." In Cortex: Statistics and Geometry of Neuronal Connectivity. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03733-1_8.

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Conference papers on the topic "In silico neurons and synapses"

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Najem, Joseph S., Graham J. Taylor, Charles P. Collier, and Stephen A. Sarles. "Synapse-Inspired Variable Conductance in Biomembranes: A Preliminary Study." In ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/smasis2017-3820.

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Memristors are solid-state devices that exhibit voltage-controlled conductance. This tunable functionality enables the implementation of biologically-inspired synaptic functions in solid-state neuromorphic computing systems. However, while memristors are meant to emulate an intricate signal transduction process performed by soft biomolecular structures, they are commonly constructed from silicon- or polymer-based materials. As a result, the volatility, intricate design, and high-energy resistance switching in memristive devices, usually, leads to energy consumption in memristors that is severa
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Ziegler, Martin, Mirko Hansen, Marina Ignatov, and Hermann Kohlstedt. "Building memristive neurons and synapses." In 2014 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2014. http://dx.doi.org/10.1109/iscas.2014.6865323.

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Xu, Peng, Timothy K. Horiuchi, Anshu Sarje, and Pamela Abshire. "Stochastic Synapse with Short-Term Depression for Silicon Neurons." In 2007 IEEE Biomedical Circuits and Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/biocas.2007.4463318.

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Zheng, Le, Sangho Shin, and Sung-Mo Steve Kang. "Memristor-based synapses and neurons for neuromorphic computing." In 2015 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2015. http://dx.doi.org/10.1109/iscas.2015.7168842.

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Ioka, Eri, Yasuyuki Matusya, and Hiroyuki Kitajima. "Bifurcation in mutually coupled three neurons with inhibitory synapses." In 2011 European Conference on Circuit Theory and Design (ECCTD). IEEE, 2011. http://dx.doi.org/10.1109/ecctd.2011.6043617.

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Lee, B. W., J. C. Lee, and B. J. Sheu. "VLSI image processor using analog programmable synapses and neurons." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137630.

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Corinto, Fernando, Valentina Lanza, Alon Ascoli, and Marco Gilli. "Synchronization in networks of FitzHugh-Nagumo neurons with memristor synapses." In 2011 European Conference on Circuit Theory and Design (ECCTD). IEEE, 2011. http://dx.doi.org/10.1109/ecctd.2011.6043616.

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Natarajan, Aishwarya, and Jennifer Hasler. "Implementation of Synapses with Hodgkin Huxley Neurons on the FPAA." In 2019 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2019. http://dx.doi.org/10.1109/iscas.2019.8702489.

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Mejías, Jorge F., and Joaquín J. Torres. "Signal detection in networks of spiking neurons with dynamical synapses." In COOPERATIVE BEHAVIOR IN NEURAL SYSTEMS: Ninth Granada Lectures. AIP, 2007. http://dx.doi.org/10.1063/1.2709589.

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Valentian, A., F. Rummens, E. Vianello, et al. "Fully Integrated Spiking Neural Network with Analog Neurons and RRAM Synapses." In 2019 IEEE International Electron Devices Meeting (IEDM). IEEE, 2019. http://dx.doi.org/10.1109/iedm19573.2019.8993431.

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Reports on the topic "In silico neurons and synapses"

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Brown, Thomas H. Self-Organization of Hebbian Synapses on Hippocampal Neurons. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada309810.

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Brown, Thomas H. Self-Organization of Hebbian Synapses on Hippocampal Neurons. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada299559.

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